human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "wireless device to device caching networks basic principles and system performance"
  Abstract: As wireless video is the fastest growing form of data traffic, methods for spectrally efficient on-demand wireless video streaming are essential to both service providers and users. A key property of video on-demand is the   asynchronous content reuse  , such that a few popular files account for a large part of the traffic but are viewed by users at different times. Caching of content on wireless devices in conjunction with device-to-device (D2D) communications allows to exploit this property, and provide a network throughput that is significantly in excess of both the conventional approach of unicasting from cellular base stations and the traditional D2D networks for “regular” data traffic. This paper presents in a   tutorial and concise form   some recent results on the throughput scaling laws of wireless networks with caching and asynchronous content reuse, contrasting the D2D approach with other alternative approaches such as conventional unicasting,   harmonic broadcasting  , and a novel   coded multicasting   approach based on caching in the user devices and network-coded transmission from the cellular base station only. Somehow surprisingly, the D2D scheme with spatial reuse and simple decentralized random caching achieves the same near-optimal throughput scaling law as coded multicasting. Both schemes achieve an unbounded throughput gain (in terms of scaling law) with respect to conventional unicasting and harmonic broadcasting, in the relevant regime where the number of video files in the library is smaller than the total size of the distributed cache capacity in the network. To better understand the relative merits of these competing approaches, we consider a holistic D2D system design incorporating traditional microwave (2 GHz) and millimeter-wave (mm-wave) D2D links; the direct connections to the base station can be used to provide those rare video requests that cannot be found in local caches. We provide extensive simulation results under a variety of system settings and compare our scheme with the systems that exploit transmission from the base station only. We show that, also in realistic conditions and nonasymptotic regimes, the proposed D2D approach offers very significant throughput gains.

Connected through hyperedge HE-1 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space"):
  with members:
    - "joint downlink scheduling for file placement and delivery in cache assisted wireless networks with finite file lifetime"
    - "optimal caching designs for perfect imperfect and unknown file popularity distributions in large scale multi tier wireless networks"
    - "online user scheduling and resource allocation for mobile edge computing systems"
    - "cooperative job dispatching in edge computing network with unpredictable uploading delay"
    - "mdp based scheduling design for mobile edge computing systems with random user arrival"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "stochastic content centric multicast scheduling for cache enabled heterogeneous cellular networks"
    - "optimal caching and user association in cache enabled heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "the design of dynamic probabilistic caching with time varying content popularity"):
  with members:
    - "decentralized caching for content delivery based on blockchain a game theoretic perspective"
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "caching policy toward maximal success probability and area spectral efficiency of cache enabled hetnets"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "optimization and analysis of probabilistic caching in n tier heterogeneous networks"
    - "cache enabled heterogeneous cellular networks optimal tier level content placement"
    - "optimal geographic caching in cellular networks"
    - "least recently used caches under the shot noise model"

Connected through hyperedge HE-3 (induced by source paper "mobility aware content placement for device to device caching systems"):
  with members:
    - "device caching for network offloading delay minimization with presence of user mobility"
    - "mobility and popularity aware coded small cell caching"
    - "mobility aware caching for content centric wireless networks modeling and methodology"
    - "mobility aware caching in d2d networks"
    - "distributed storage in mobile wireless networks with device to device communication"
    - "optimal storage allocation for wireless cloud caching systems with a limited sum storage capacity"
    - "caching placement in stochastic wireless caching helper networks channel selection diversity via caching"
    - "what will 5g be"

Connected through hyperedge HE-4 (induced by source paper "cache allocations for consecutive requests of categorized contents service provider s perspective"):
  with members:
    - "wireless video caching and dynamic streaming under differentiated quality requirements"
    - "optimization and analysis of probabilistic caching in n tier heterogeneous networks"
    - "optimal geographic caching in cellular networks"
    - "femtocaching wireless video content delivery through distributed caching helpers"

Connected through hyperedge HE-5 (induced by source paper "cache optimization models and algorithms"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "a reinforcement learning approach to proactive caching in wireless networks"

Connected through hyperedge HE-6 (induced by source paper "content caching and delivery in wireless radio access networks"):
  with members:
    - "mobility aware coded storage and delivery"
    - "cache aided fog radio access networks with partial connectivity"
    - "a converse bound for cache aided interference networks"
    - "treating content delivery in multi antenna coded caching as general message sets transmission a dof region perspective"
    - "fundamental limits of decentralized caching in fog rans with wireless fronthaul"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "decentralized coded caching in wireless networks trade off between storage and latency"
    - "online edge caching and wireless delivery in fog aided networks with dynamic content popularity"

Connected through hyperedge HE-7 (induced by source paper "device to device coded caching with distinct cache sizes"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "device to device secure coded caching"
    - "on coded caching with heterogeneous distortion requirements"
    - "coded caching for heterogeneous systems an optimization perspective"
    - "coded caching with heterogeneous cache sizes and link qualities the two user case"
    - "optimization of heterogeneous coded caching"
    - "coded caching in partially cooperative d2d communication networks"
    - "a novel index coding scheme and its application to coded caching"

Connected through hyperedge HE-8 (induced by source paper "individual preference aware caching policy design in wireless d2d networks"):
  with members:
    - "throughput outage analysis and evaluation of cache aided d2d networks with measured popularity distributions"
    - "modeling and performance analysis of full duplex communications in cache enabled d2d networks"
    - "learning based delay aware caching in wireless d2d caching networks"
    - "caching policy optimization for d2d communications by learning user preference"
    - "high throughput opportunistic cooperative device to device communications with caching"
    - "cache enabled device to device communications offloading gain and energy cost"
    - "on consideration of content preference and sharing willingness in d2d assisted offloading"
    - "optimizing content caching to maximize the density of successful receptions in device to device networking"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)