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 "caching in wireless networks"):
  Abstract: We consider the problem of delivering content cached in a wireless network of n nodes randomly located on a square of area n. The network performance is described by the n2^n-dimensional caching capacity region of the wireless network. We provide an inner bound on this caching capacity region, and, in the high path-loss regime, a matching (in the scaling sense) outer bound. For large path-loss exponent, this provides an information-theoretic scaling characterization of the entire caching capacity region. The proposed communication scheme achieving the inner bound shows that the problems of cache selection and channel coding can be solved separately without loss of order-optimality. On the other hand, our results show that the common architecture of nearest-neighbor cache selection can be arbitrarily bad, implying that cache selection and load balancing need to be performed jointly.

Includes members:
  Member "linear capacity scaling in wireless networks beyond physical limits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas")
  Member "optimality and approximate optimality of source channel separation in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a new approach to lossy network compression of a tuple of correlated multivariate gaussian rvs")
  Member "the balanced unicast and multicast capacity regions of large wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how much cache is needed to achieve linear capacity scaling in backhaul limited dense wireless networks")
  Member "on capacity scaling in arbitrary wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "throughput scaling of covert communication over wireless adhoc networks")
  Member "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wireless mesh networking with devices equipped with multi connectivity")
  Member "network information flow with correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity bounds for networks with correlated sources and characterisation of distributions by entropies")

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)