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: "caching at base stations with heterogeneous user demands and spatial locality"
  Abstract: The existing proactive caching policies are designed by assuming that all users request contents with identical activity level at uniformly distributed or known locations, among which most of the policies are optimized by assuming that user preference is identical to content popularity. However, these assumptions are not true based on the recent data analysis. In this paper, we investigate what happens without these assumptions. To this end, we establish a framework to optimize caching policy for base stations exploiting heterogeneous user preference, activity level, and spatial locality. We derive success probability and average rate of each user as utility function, respectively, and obtain the optimal caching policy maximizing a weighted sum of average utility (reflecting network performance) and minimal utility of users (reflecting user fairness). To investigate the intertwined impact of individual user request behavior on caching, we provide an algorithm to synthesize user preference from given content popularity and activity level with controlled preference similarity and validate the algorithm with the real datasets. Analysis and simulation results show that exploiting individual user behavior can improve both network performance and user fairness, and the gain increases with the skewness of spatial locality, and the heterogeneity of user preference and activity level.

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"
    - "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"
    - "optimal geographic caching in cellular networks with linear content coding"
    - "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 "caching at base stations with multi cluster multicast wireless backhaul via accelerated first order algorithm"):
  with members:
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "noisy broadcast networks with receiver caching"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "cloud radio access networks with coded caching"
    - "analysis and optimization of caching and multicasting in large scale cache enabled wireless networks"

Connected through hyperedge HE-3 (induced by source paper "a personalized preference learning framework for caching in mobile networks"):
  with members:
    - "user preference learning based edge caching for fog radio access network"
    - "on the effects of subpacketization in content centric mobile networks"
    - "how to cache in mobile hybrid iot networks"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "pricing and resource allocation via game theory for a small cell video caching system"
    - "information theoretic caching the multi user case"
    - "caching incentive design in wireless d2d networks a stackelberg game approach"
    - "a learning based approach to caching in heterogenous small cell networks"

Connected through hyperedge HE-4 (induced by source paper "model free unsupervised learning for optimization problems with constraints"):
  with members:
    - "learning to optimize with unsupervised learning training deep neural networks for urllc"
    - "unsupervised deep learning for ultra reliable and low latency communications"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"

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)