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 "scalable capacity bounding models for wireless networks"):
  Abstract: The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper (resp. lower) bounding models, which can be used to calculate the outer (resp. inner) bounds for the capacity region of the original noisy network. Based on the network equivalence framework, this paper presents scalable upper and lower bounding models for wireless networks with potentially many nodes. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels (MACs) and broadcast channels (BCs). The upper bounding model, consisting of only point-to-point bit pipes, is constructed by firstly extending the "one-shot" upper bounding models developed by Calmon et al. and then integrating them with network equivalence tools. The lower bounding model, consisting of both point-to-point and point-to-points bit pipes, is constructed based on a two-step update of the lower bounding models to incorporate the broadcast nature of wireless transmission. The main advantages of the proposed methods are their simplicity and the fact that they can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper and lower bounds can approach the capacity in some setups.

Includes members:
  Member "the likelihood encoder for lossy compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keyless covert communication via channel state information")
  Member "a generalized typicality for abstract alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the renyi capacity and center")
  Member "distributed channel synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "capacity of multiple unicast in wireless networks a polymatroidal approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near optimal quantization and linear network coding for relay networks")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "separation of source network coding and channel coding in wireline networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constrained source coding with side information")
  Member "path gain algebraic formulation for the scalar linear network coding problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a class of non linearly solvable networks")
  Member "optimality and approximate optimality of source channel separation in networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a new approach to lossy network compression of a tuple of correlated multivariate gaussian rvs")

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)