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: "joint pushing and caching with a finite receiver buffer optimal policies and throughput analysis"
  Abstract: Pushing and caching hold the promise of significantly increasing the throughput of content-centric wireless networks. However, the throughput gain of these techniques is limited by the buffer size of the receiver. To overcome this, this paper presents a Joint Pushing and Caching (JPC) method that jointly determines the contents to be pushed to, and to be removed from, the receiver buffer in each timeslot. An offline and two online JPC policies are proposed respectively based on noncausal, statistical, and causal content Request Delay Information (RDI), which predicts a user's request time for certain content. It is shown that the effective throughput of JPC is increased with the receiver buffer size and the pushing channel capacity. Furthermore, the causal feedback of user requests is found to greatly enhance the performance of online JPC without inducing much signalling overhead in practice.

Connected through hyperedge HE-1 (induced by source paper "a reinforcement learning approach to proactive caching in wireless networks"):
  with members:
    - "learning based caching in cloud aided wireless networks"
    - "energy efficient wireless content delivery with proactive caching"
    - "a deep reinforcement learning based framework for content caching"
    - "stochastic content centric multicast scheduling for cache enabled heterogeneous cellular networks"
    - "wireless content caching for small cell and d2d networks"
    - "placing dynamic content in caches with small population"
    - "learning based optimization of cache content in a small cell base station"
    - "living on the edge the role of proactive caching in 5g wireless networks"

Connected through hyperedge HE-2 (induced by source paper "delay analysis and optimization in cache enabled multi cell cooperative networks"):
  with members:
    - "the three primary colors of mobile systems"
    - "optimal caching placement for d2d assisted wireless caching networks"
    - "analysis on cache enabled wireless heterogeneous networks"
    - "a provably efficient online collaborative caching algorithm for multicell coordinated systems"
    - "on content centric wireless delivery networks"
    - "fundamental limits of caching"

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)