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 "efficient capacity computation and power optimization for relay networks"):
  Abstract: The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes. #R##N#For Gaussian networks, cut-set capacities are also functions of the powers assigned to the nodes. We consider a family of power optimization problems and show that they can be solved in polynomial time. In particular, we show that the minimization of the sum of powers assigned to the nodes subject to a minimum rate constraint (measured in terms of cut-set bounds) can be computed in polynomial time. We propose an heuristic algorithm to solve this problem and measure its performance through simulations on random Gaussian networks. We observe that in the optimal allocations most of the power is assigned to a small subset of relays, which suggests that network simplification may be possible without excessive performance degradation.

Includes members:
  Member "near optimal nonmyopic value of information in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "improved combinatorial algorithms for wireless information flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bounded contention coding for wireless networks in the high snr regime")
  Member "approximately achieving gaussian relay network capacity with lattice codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks")
  Member "combinatiorial algorithms for wireless information flow":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows")
  Member "a combinatorial study of linear deterministic relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deterministic polynomial time algorithm for constructing a multicast coding scheme for linear deterministic relay networks")
  Member "approximate capacity of gaussian relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multihop backhaul compression for the uplink of cloud radio access networks")
  Member "a deterministic approach to wireless relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the deterministic sum capacity of the multiple access wiretap channel")

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)