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: "energy efficiency in cache enabled small cell networks with adaptive user clustering"
  Abstract: The exponential increase in mobile data traffic forces network operators to deal with a capacity shortage. One of the most promising technologies for 5G networks is proactive caching. Using a network of cache enabled small cells, traffic during peak hours can be reduced through proactively caching the content that is most probable to be requested. Studying the users behavior and caching files at base stations accordingly, can offload the backhaul traffic and improve the network throughput. We explore a new caching framework, in which, the users are clustered according to their content popularity. The caching is then done based on the mean content popularity in each cluster. In order to achieve an efficient clustering of the users, we use a statistical model selection criterion, namely the Akaike information criterion. We derive a closed form expression of the hit probability, which will be optimized with respect to the fractions of small base stations associated to each cluster. We then investigate the Energy Efficiency of the proposed caching framework. We derive a closed form expression of the energy efficiency, which will be optimized by defining the optimal density of active small base stations. We then provide a small base station allocation algorithm in order to associate each individual base station with a given cluster. This algorithm aims at caching in each small base station the files that are most likely to be requested within their direct neighborhood. Coupled with channel inversion power control, this optimization will improve the energy efficiency and cache hit probability of the network. Numerical results show that the clustering scheme enable to considerably improve the cache hit probability. We also show that optimizing the allocation of the small base stations results in improving of the energy efficiency and hit probability in the network.

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)