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: "optimal relay functionality for snr maximization in memoryless relay networks"
  Abstract: We explore the SNR-optimal relay functionality in a \emph{memoryless} relay network, i.e. a network where, during each channel use, the signal transmitted by a relay depends only on the last received symbol at that relay. We develop a generalized notion of SNR for the class of memoryless relay functions. The solution to the generalized SNR optimization problem leads to the novel concept of minimum mean square uncorrelated error estimation(MMSUEE). For the elemental case of a single relay, we show that MMSUEE is the SNR-optimal memoryless relay function regardless of the source and relay transmit power, and the modulation scheme. This scheme, that we call estimate and forward (EF), is also shown to be SNR-optimal with PSK modulation in a parallel relay network. We demonstrate that EF performs better than the best of amplify and forward (AF) and demodulate and forward (DF), in both parallel and serial relay networks. We also determine that AF is near-optimal at low transmit power in a parallel network, while DF is near-optimal at high transmit power in a serial network. For hybrid networks that contain both serial and parallel elements, and when robust performance is desired, the advantage of EF over the best of AF and DF is found to be significant. Error probabilities are provided to substantiate the performance gain obtained through SNR optimality. We also show that, for \emph{Gaussian} inputs, AF, DF and EF become identical.

Connected through hyperedge HE-1 (induced by source paper "optimized transmission for consensus in wireless sensor networks"):
  with members:
    - "massive mimo for wireless sensing with a coherent multiple access channel"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "designing unimodular codes via quadratic optimization"
    - "estimation in phase shift and forward wireless sensor networks"

Connected through hyperedge HE-2 (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"
    - "amplify and forward in wireless relay networks"
    - "network beamforming using relays with perfect channel information"

Connected through hyperedge HE-3 (induced by source paper "beam forming for secure communication in amplify and forward networks an snr based approach"):
  with members:
    - "collaborative relay beamforming for secrecy"

Connected through hyperedge HE-4 (induced by source paper "distributed soft coding with a soft input soft output siso relay encoder in parallel relay channels"):
  with members:
    - "distributed raptor coding for erasure channels partially and fully coded cooperation"
    - "performance analysis of hybrid relay selection in cooperative wireless systems"
    - "bilayer low density parity check codes for decode and forward in relay channels"

Connected through hyperedge HE-5 (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"
    - "amplify and forward in wireless relay networks"
    - "wireless network simplification the gaussian n relay diamond network"
    - "a simple cooperative diversity method based on network path selection"

Connected through hyperedge HE-6 (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"
    - "amplify and forward in wireless relay networks"
    - "wireless network simplification the gaussian n relay diamond network"

Connected through hyperedge HE-7 (induced by source paper "analog network coding in general snr regime"):
  with members:
    - "analog network coding in the generalized high snr regime"
    - "amplify and forward in wireless relay networks"

Connected through hyperedge HE-8 (induced by source paper "network code design for orthogonal two hop network with broadcasting relay a joint source channel network coding approach"):
  with members:
    - "coding for errors and erasures in random network coding"
    - "network coding over a noisy relay a belief propagation approach"

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)