human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the age of information in internet of things systems with correlated devices")
  Member "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach")
  Member "caching improvement using adaptive user clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "echo state networks for proactive caching in cloud based radio access networks with mobile users")
  Member "energy efficiency of downlink networks with caching at base stations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing")
  Member "greendelivery proactive content caching and push with energy harvesting based small cells":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the effective throughput of coded caching a game theoretic perspective")
  Member "on the benefits of edge caching for mimo interference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "application of machine learning in wireless networks key techniques and open issues")
  Member "optimal geographic caching in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online convex optimization for caching networks")
  Member "caching at the edge a green perspective for 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "tile based joint caching and delivery of 360 o videos in heterogeneous networks")

Question: Please tell me which class the source paper 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)