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: "a low complexity approach to distributed cooperative caching with geographic constraints"
  Abstract: We consider caching in cellular networks in which each base station is equipped with a cache that can store a limited number of files. The popularity of the files is known and the goal is to place files in the caches such that the probability that a user at an arbitrary location in the plane will find the file that she requires in one of the covering caches is maximized. #R##N#We develop distributed asynchronous algorithms for deciding which contents to store in which cache. Such cooperative algorithms require communication only between caches with overlapping coverage areas and can operate in asynchronous manner. The development of the algorithms is principally based on an observation that the problem can be viewed as a potential game. Our basic algorithm is derived from the best response dynamics. We demonstrate that the complexity of each best response step is independent of the number of files, linear in the cache capacity and linear in the maximum number of base stations that cover a certain area. Then, we show that the overall algorithm complexity for a discrete cache placement is polynomial in both network size and catalog size. In practical examples, the algorithm converges in just a few iterations. Also, in most cases of interest, the basic algorithm finds the best Nash equilibrium corresponding to the global optimum. We provide two extensions of our basic algorithm based on stochastic and deterministic simulated annealing which find the global optimum. #R##N#Finally, we demonstrate the hit probability evolution on real and synthetic networks numerically and show that our distributed caching algorithm performs significantly better than storing the most popular content, probabilistic content placement policy and Multi-LRU caching policies.

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 "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-3 (induced by source paper "learning to cooperate in d2d caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "wireless caching technical misconceptions and business barriers"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "efficient and fair collaborative mobile internet access"

Connected through hyperedge HE-4 (induced by source paper "online learning models for content popularity prediction in wireless edge caching"):
  with members:
    - "user preference learning based edge caching for fog radio access network"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "caching policy toward maximal success probability and area spectral efficiency of cache enabled hetnets"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "interference functionals in poisson networks"
    - "optimization of caching devices with geometric constraints"
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-5 (induced by source paper "distributed cooperative caching for vod with geographic constraints"):
  with members:
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-6 (induced by source paper "implicit coordination of caches in small cell networks under unknown popularity profiles"):
  with members:
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "spatial multi lru caching for wireless networks with coverage overlaps"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"
    - "efficient analysis of caching strategies under dynamic content popularity"
    - "a versatile and accurate approximation for lru cache performance"
    - "femtocaching wireless video content delivery through distributed caching helpers"

Connected through hyperedge HE-7 (induced by source paper "multi agent learning for cooperative large scale caching networks"):
  with members:
    - "spatial multi lru caching for wireless networks with coverage overlaps"
    - "content caching and delivery over heterogeneous wireless networks"
    - "learning based optimization of cache content in a small cell base station"
    - "on the complexity of optimal routing and content caching in heterogeneous networks"
    - "analyzing the performance of lru caches under non stationary traffic patterns"

Connected through hyperedge HE-8 (induced by source paper "gibbsian on line distributed content caching strategy for cellular networks"):
  with members:
    - "caching policy toward maximal success probability and area spectral efficiency of cache enabled hetnets"
    - "on optimal geographical caching in heterogeneous cellular networks"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "edge caching for coverage and capacity aided heterogeneous networks"
    - "on the delay of geographical caching methods in two tiered heterogeneous networks"
    - "placing dynamic content in caches with small population"
    - "optimization of caching devices with geometric constraints"
    - "optimal geographic caching in cellular networks"

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)