human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "amplify and forward in wireless relay networks"
  Abstract: A general class of wireless relay networks with a single source-destination pair is considered. Intermediate nodes in the network employ an amplify-and-forward scheme to relay their input signals. In this case the overall input-output channel from the source via the relays to the destination effectively behaves as an intersymbol interference channel with colored noise. Unlike previous work we formulate the problem of the maximum achievable rate in this setting as an optimization problem with no assumption on the network size, topology, and received signal-to-noise ratio. Previous work considered only scenarios wherein relays use all their power to amplify their received signals. We demonstrate that this may not always maximize the maximal achievable rate in amplify-and-forward relay networks. The proposed formulation allows us to not only recover known results on the performance of the amplify-and-forward schemes for some simple relay networks but also characterize the performance of more complex amplify-and-forward relay networks which cannot be addressed in a straightforward manner using existing approaches. #R##N#Using cut-set arguments, we derive simple upper bounds on the capacity of general wireless relay networks. Through various examples, we show that a large class of amplify-and-forward relay networks can achieve rates within a constant factor of these upper bounds asymptotically in network parameters.

Connected through hyperedge HE-1 (induced by source paper "distributed decode forward for relay networks"):
  with members:
    - "partial decode forward relaying for the gaussian two hop relay network"
    - "noisy network coding with partial df"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "index coding capacity how far can one go with only shannon inequalities"
    - "short message noisy network coding with a decode forward option"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"

Connected through hyperedge HE-2 (induced by source paper "secure transmission in amplify and forward diamond networks with a single eavesdropper"):
  with members:
    - "optimal amplify and forward schemes for relay channels with correlated relay noise"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "relay beamforming strategies for physical layer security"
    - "discrete memoryless interference and broadcast channels with confidential messages secrecy rate regions"
    - "network beamforming using relays with perfect channel information"

Connected through hyperedge HE-3 (induced by source paper "coding schemes for discrete memoryless multicast networks with rate limited feedback"):
  with members:
    - "partial decode forward relaying for the gaussian two hop relay network"
    - "short message noisy network coding with a decode forward option"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "slepian wolf coding over cooperative relay networks"

Connected through hyperedge HE-4 (induced by source paper "analog network coding in nonlinear chains"):
  with members:
    - "analog network coding in general snr regime"
    - "analog network coding in general snr regime performance of a greedy scheme"
    - "analog network coding in the generalized high snr regime"
    - "network beamforming using relays with perfect channel information"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-5 (induced by source paper "secure transmission in amplify and forward networks for multiple degraded eavesdroppers"):
  with members:
    - "collaborative relay beamforming for secrecy"
    - "towards the secrecy capacity of the gaussian mimo wire tap channel the 2 2 1 channel"
    - "transmitter optimization for achieving secrecy capacity in gaussian mimo wiretap channels"
    - "network beamforming using relays with perfect channel information"

Connected through hyperedge HE-6 (induced by source paper "analog network coding in general snr regime performance of network simplification"):
  with members:
    - "analog network coding in general snr regime"
    - "analog network coding in general snr regime performance of a greedy scheme"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "analog network coding in the generalized high snr regime"
    - "wireless network simplification the gaussian n relay diamond network"
    - "a simple cooperative diversity method based on network path selection"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-7 (induced by source paper "analog network coding in general snr regime performance of a greedy scheme"):
  with members:
    - "analog network coding in general snr regime"
    - "analog network coding in the generalized high snr regime"
    - "wireless network simplification the gaussian n relay diamond network"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-8 (induced by source paper "analog network coding in general snr regime"):
  with members:
    - "analog network coding in the generalized high snr regime"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Question: Please tell me which class the center node 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)