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: "bayesian quantized network coding via belief propagation"
  Abstract: In this paper, we propose an alternative for routing based packet forwarding, which uses network coding to increase transmission efficiency, in terms of both compression and error resilience. This non-adaptive encoding is called quantized network coding, which involves random linear mapping in the real field, followed by quantization to cope with the finite capacity of the links. At the gateway node, which collects received quantized network coder packets, minimum mean squared error decoding is performed, by using belief propagation in the factor graph representation. Our simulation results show a significant improvement, in terms of the number of required packets to recover the messages, which can be interpreted as an embedded distributed source coding for correlated messages.

Connected through hyperedge HE-1 (induced by source paper "non adaptive distributed compression in networks"):
  with members:
    - "one step quantized network coding for near sparse gaussian messages"
    - "quantized network coding for sparse messages"
    - "restricted isometry property in quantized network coding of sparse messages"
    - "compressive sensing over networks"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "quantized network coding for correlated sources"):
  with members:
    - "non adaptive distributed compression in networks"
    - "one step quantized network coding for near sparse gaussian messages"
    - "quantized network coding for sparse messages"
    - "restricted isometry property in quantized network coding of sparse messages"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "compressive sensing over graphs"
    - "compressive sensing over networks"

Connected through hyperedge HE-3 (induced by source paper "one step quantized network coding for near sparse gaussian messages"):
  with members:
    - "quantized network coding for sparse messages"
    - "restricted isometry property in quantized network coding of sparse messages"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "bayesian compressive sensing via belief propagation"

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)