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 "opportunistic relaying in wireless networks"):
  Abstract: Relay networks having n source-to-destination pairs and m half-duplex relays, all operating in the same frequency band and in the presence of block fading, are analyzed. This setup has attracted significant attention, and several relaying protocols have been reported in the literature. However, most of the proposed solutions require either centrally coordinated scheduling or detailed channel state information (CSI) at the transmitter side. Here, an opportunistic relaying scheme is proposed that alleviates these limitations, without sacrificing the system throughput scaling in the regime of large n. The scheme entails a two-hop communication protocol, in which sources communicate with destinations only through half-duplex relays. All nodes operate in a completely distributed fashion, with no cooperation. The key idea is to schedule at each hop only a subset of nodes that can benefit from multiuser diversity. To select the source and destination nodes for each hop, CSI is required at receivers (relays for the first hop, and destination nodes for the second hop), and an index-valued CSI feedback at the transmitters. For the case when n is large and m is fixed, it is shown that the proposed scheme achieves a system throughput of m/2 bits/s/Hz. In contrast, the information-theoretic upper bound of (m/2) log log n bits/s/Hz is achievable only with more demanding CSI assumptions and cooperation between the relays. Furthermore, it is shown that, under the condition that the product of block duration and system bandwidth scales faster than log n log log n, the achievable throughput of the proposed scheme scales as Theta (log n). Notably, this is proven to be the optimal throughput scaling even if centralized scheduling is allowed, thus proving the optimality of the proposed scheme in the scaling law sense. Simulation results indicate a rather fast convergence to the asymptotic limits with the system's size, demonstrating the practical importance of the scaling results.

Includes members:
  Member "throughput scaling of wireless networks with random connections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "caching gain in wireless networks with fading a multi user diversity perspective")
  Member "crystallization in large wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "apply artificial noise to secure fading relay networks a ser based approach")
  Member "a simple cooperative diversity method based on network path selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynwalks global topology and recent changes awareness dynamic network embedding")
  Member "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wireless mesh networking with devices equipped with multi connectivity")

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)