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 "analog network coding in general snr regime performance of network simplification"):
  Abstract: We consider a communication scenario where a source communicates with a destination over a directed layered relay network. Each relay performs analog network coding where it scales and forwards the signals received at its input. In this scenario, we address the question: What portion of the maximum end-to-end achievable rate can be maintained if only a fraction of relay nodes available at each layer are used? #R##N#We consider, in particular, the Gaussian diamond network (layered network with a single layer of relay nodes) and a class of symmetric layered networks. For these networks we show that each relay layer increases the additive gap between the optimal analog network coding performance with and without network simplification (using k instead of N relays in each layer, k < N) by no more than log(N/k)^2 bits and the corresponding multiplicative gap by no more than a factor of (N/k)^2, asymptotically (in source power). To the best of our knowledge, this work offers the first characterization of the performance of network simplification in general layered amplify-and-forward relay networks. Further, unlike most of the current approximation results that attempt to bound optimal rates either within an additive gap or a multiplicative gap, our results suggest a new rate approximation scheme that allows for the simultaneous computation of additive and multiplicative gaps.

Includes members:
  Member "analog network coding in general snr regime":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network simplification for secure af relaying")
  Member "analog network coding in general snr regime performance of a greedy scheme":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analog network coding in nonlinear chains")
  Member "the approximate capacity of the gaussian n relay diamond network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks")
  Member "analog network coding in the generalized high snr regime":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analog network coding in nonlinear chains")
  Member "amplify and forward in wireless relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed decode forward for relay networks")
  Member "wireless network simplification the gaussian n relay diamond network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "a simple cooperative diversity method based on network path selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynwalks global topology and recent changes awareness dynamic network embedding")
  Member "optimal relay functionality for snr maximization in memoryless relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimized transmission for consensus in wireless sensor 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)