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: "network coding over a noisy relay a belief propagation approach"
  Abstract: In recent years, network coding has been investigated as a method to obtain improvements in wireless networks. A typical assumption of previous work is that relay nodes performing network coding can decode the messages from sources perfectly. On a simple relay network, we design a scheme to obtain network coding gain even when the relay node cannot perfectly decode its received messages. In our scheme, the operation at the relay node resembles message passing in belief propagation, sending the logarithm likelihood ratio (LLR) of the network coded message to the destination. Simulation results demonstrate the gain obtained over different channel conditions. The goal of this paper is not to give a theoretical result, but to point to possible interaction of network coding with user cooperation in noisy scenario. The extrinsic information transfer (EXIT) chart is shown to be a useful engineering tool to analyze the performance of joint channel coding and network coding in the network.

Connected through hyperedge HE-1 (induced by source paper "performance analysis of random linear network coding in two source single relay networks"):
  with members:
    - "binary systematic network coding for progressive packet decoding"

Connected through hyperedge HE-2 (induced by source paper "soft decoding based strategies for relay and interference channels analysis and achievable rates using ldpc codes"):
  with members:
    - "on the performance of low density parity check codes for gaussian interference channels"
    - "mmse of bad codes"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "a theory of network equivalence parts i and ii"
    - "compute and forward harnessing interference through structured codes"
    - "joint physical layer coding and network coding for bi directional relaying"
    - "gaussian interference channel capacity to within one bit"
    - "the generalized area theorem and some of its consequences"

Connected through hyperedge HE-3 (induced by source paper "on the design of a novel joint network channel coding scheme for the multiple access relay channel"):
  with members:
    - "opportunistic communications in an orthogonal multiaccess relay channel"

Connected through hyperedge HE-4 (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"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-5 (induced by source paper "improving two way selective decode and forward wireless relaying with energy efficient one bit soft forwarding"):
  with members:
    - "physical layer network coding tutorial survey and beyond"
    - "an overview of ieee 802 15 6 standard"

Connected through hyperedge HE-6 (induced by source paper "joint channel network coding strategies for networks with low complexity relays"):
  with members:
    - "bilayer low density parity check codes for decode and forward in relay channels"

Connected through hyperedge HE-7 (induced by source paper "decoding network codes by message passing"):
  with members:
    - "coding for network coding"

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)