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 "wireless network simplification the performance of routing"):
  Abstract: Consider a wireless Gaussian network where a source wishes to communicate with a destination with the help of N full-duplex relay nodes. Most practical systems today route information from the source to the destination using the best path that connects them. In this paper, we show that routing can in the worst case result in an unbounded gap from the network capacity - or reversely, physical layer cooperation can offer unbounded gains over routing. More specifically, we show that for $N$-relay Gaussian networks with an arbitrary topology, routing can in the worst case guarantee an approximate fraction $\frac{1}{\left\lfloor N/2 \right\rfloor + 1}$ of the capacity of the full network, independently of the SNR regime. We prove that this guarantee is fundamental, i.e., it is the highest worst-case guarantee that we can provide for routing in relay networks. Next, we consider how these guarantees are refined for Gaussian layered relay networks with $L$ layers and $N_L$ relays per layer. We prove that for arbitrary $L$ and $N_L$, there always exists a route in the network that approximately achieves at least $\frac{2}{(L-1)N_L + 4}$ $\left(\mbox{resp.}\frac{2}{LN_L+2}\right)$ of the network capacity for odd $L$ (resp. even $L$), and there exist networks where the best routes exactly achieve these fractions. These results are formulated within the network simplification framework, that asks what fraction of the capacity we can achieve by using a subnetwork (in our case, a single path). A fundamental step in our proof is a simplification result for MIMO antenna selection that may also be of independent interest. To the best of our knowledge, this is the first result that characterizes, for general wireless network topologies, what is the performance of routing with respect to physical layer cooperation techniques that approximately achieve the network capacity.

Includes members:
  Member "network simplification in half duplex building on submodularity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "half duplex routing is np hard")
  Member "network simplification for secure af relaying":
    also connected through other hyperedges:
      (none)
  Member "consistency in the face of change an adaptive approach to physical layer cooperation":
    also connected through other hyperedges:
      (none)
  Member "cut set bound is loose for gaussian relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the arbitrarily varying relay channel")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "wireless network simplification the gaussian n relay diamond network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "dynwalks global topology and recent changes awareness dynamic network embedding")

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)