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: "optimal geographic caching in cellular networks with linear content coding"
  Abstract: We state and solve a problem of the optimal geographic caching of content in cellular networks, where linear combinations of contents are stored in the caches of base stations. We consider a general content popularity distribution and a general distribution of the number of stations covering the typical location in the network. We are looking for a policy of content caching maximizing the probability of serving the typical content request from the caches of covering stations. The problem has a special form of monotone sub-modular set function maximization. Using dynamic programming, we find a deterministic policy solving the problem. We also consider two natural greedy caching policies. We evaluate our policies considering two popular stochastic geometric coverage models: the Boolean one and the Signal-to-Interference-and-Noise-Ratio one, assuming Zipf popularity distribution. Our numerical results show that the proposed deterministic policies are in general not worse than some randomized policy considered in the literature and can further improve the total hit probability in the moderately high coverage regime.

Connected through hyperedge HE-1 (induced by source paper "optimal scheduling of content caching subject to deadline"):
  with members:
    - "on optimal proactive and retention aware caching with user mobility"
    - "caching at base stations with heterogeneous user demands and spatial locality"
    - "a rolling horizon dynamic programming approach for collaborative caching"
    - "accounting for information freshness in scheduling of content caching"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "caching with time varying popularity profiles a learning theoretic perspective"
    - "cost optimal caching for d2d networks with user mobility modeling analysis and computational approaches"
    - "a learning based approach to caching in heterogenous small cell networks"

Connected through hyperedge HE-2 (induced by source paper "accounting for information freshness in scheduling of content caching"):
  with members:
    - "dynamic content updates in heterogeneous wireless networks"
    - "age of information aware cache updating with file and age dependent update durations"
    - "on optimal proactive and retention aware caching with user mobility"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "caching with time varying popularity profiles a learning theoretic perspective"
    - "cost optimal caching for d2d networks with user mobility modeling analysis and computational approaches"
    - "update or wait how to keep your data fresh"
    - "capacity of cellular networks with femtocache"

Connected through hyperedge HE-3 (induced by source paper "spatially correlated content caching for device to device communications"):
  with members:
    - "how much cache is needed to achieve linear capacity scaling in backhaul limited dense wireless networks"
    - "fundamentals of modeling finite wireless networks using binomial point process"
    - "optimizing content caching to maximize the density of successful receptions in device to device networking"
    - "spatial multi lru caching for wireless networks with coverage overlaps"
    - "placing dynamic content in caches with small population"
    - "modeling and performance analysis of clustered device to device networks"
    - "optimizing the spatial content caching distribution for device to device communications"
    - "optimizing spatial throughput in device to device 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)