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 "graph based cooperative caching in fog ran"):
  Abstract: In this paper, the cooperative caching problem in fog radio access networks (F-RAN) is investigated. To maximize the incremental offloaded traffic, we formulate the clustering optimization problem with the consideration of cooperative caching and local content popularity, which falls into the scope of combinatorial programming. % and is NP-hard. We then propose an effective graph-based approach to solve this challenging problem. Firstly, a node graph is constructed with its vertex set representing the considered fog access points (F-APs) and its edge set reflecting the potential cooperations among the F-APs. %whether the F-APs the distance and load difference among the F-APs. Then, by exploiting the adjacency table of each vertex of the node graph, we propose to get the complete subgraphs through indirect searching for the maximal complete subgraphs for the sake of a reduced searching complexity. Furthermore, by using the complete subgraphs so obtained, a weighted graph is constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the original clustering optimization problem can be transformed into an equivalent 0-1 integer programming problem. The max-weight independent subset of the vertex set of the weighted graph, which is equivalent to the objective cluster sets, can then be readily obtained by solving the above optimization problem through the greedy algorithm that we propose. Our proposed graph-based approach has an apparently low complexity in comparison with the brute force approach which has an exponential complexity. Simulation results show the remarkable improvements in terms of offloading gain by using our proposed approach.

Includes members:
  Member "context aware proactive content caching with service differentiation in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing")
  Member "fog computing based radio access networks issues and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach")
  Member "dynamic clustering and user association in wireless small cell networks with social considerations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "cooperative caching and transmission design in cluster centric small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "content centric sparse multicast beamforming for cache enabled cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "content aware user clustering and caching in wireless small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cooperative caching in fog radio access networks a graph based approach")
  Member "message passing for maximum weight independent set":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "throughput optimal decentralized scheduling with single bit state feedback for a class of queueing systems")

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)