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: "placing dynamic content in caches with small population"
  Abstract: This paper addresses a fundamental limitation for the adoption of caching for wireless access networks due to small population sizes. This shortcoming is due to two main challenges: making timely estimates of varying content popularity and inferring popular content from small samples. We propose a framework which alleviates such limitations. To timely estimate varying popularity in a context of a single cache we propose an Age-Based Threshold (ABT) policy which caches all contents requested more times than a threshold N (τ), where τ is the content age. We show that ABT is asymptotically hit rate optimal in the many contents regime, which allows us to obtain the first characterization of the optimal performance of a caching system in a dynamic context. We then address small sample sizes focusing on L local caches and one global cache. On the one hand we show that the global cache learns L times faster by aggregating all requests from local caches, which improves hit rates. On the other hand, aggregation washes out local characteristics of correlated traffic which penalizes hit rate. This motivates coordination mechanisms which combine global learning of popularity scores in clusters and Least-Recently-Used (LRU) policy with prefetching.

Connected through hyperedge HE-1 (induced by source paper "online convex optimization for caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "learning to cooperate in d2d caching networks"
    - "cache optimization models and algorithms"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"

Connected through hyperedge HE-2 (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"
    - "wireless device to device caching networks basic principles and system performance"

Connected through hyperedge HE-3 (induced by source paper "collaborative multi agent multi armed bandit learning for small cell caching"):
  with members:
    - "content popularity prediction towards location aware mobile edge caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "a deep reinforcement learning based framework for content caching"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "caching placement in stochastic wireless caching helper networks channel selection diversity via caching"
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "optimal caching designs for perfect imperfect and unknown file popularity distributions in large scale multi tier wireless networks"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "a deep reinforcement learning based framework for content caching"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "optimization and analysis of probabilistic caching in n tier heterogeneous networks"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "cooperative caching and transmission design in cluster centric small cell networks"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"):
  with members:
    - "joint switch upgrade and controller deployment in hybrid software defined networks"
    - "bandit online learning with unknown delays"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "decentralized caching for content delivery based on blockchain a game theoretic perspective"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-7 (induced by source paper "joint downlink scheduling for file placement and delivery in cache assisted wireless networks with finite file lifetime"):
  with members:
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "stochastic content centric multicast scheduling for cache enabled heterogeneous cellular networks"
    - "wireless device to device caching networks basic principles and system performance"
    - "optimal caching and user association in cache enabled heterogeneous wireless networks"
    - "mobility aware caching for content centric wireless networks modeling and methodology"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "caching placement in stochastic wireless caching helper networks channel selection diversity via caching"
    - "cache enabled heterogeneous cellular networks optimal tier level content placement"

Connected through hyperedge HE-8 (induced by source paper "learning to cache with no regrets"):
  with members:
    - "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"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "optimal geographic caching in cellular networks"
    - "adapting caching to audience retention rate which video chunk to store"

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.NI(Networking and Internet Architecture)