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: "one step quantized network coding for near sparse gaussian messages"
  Abstract: In this paper, mathematical bases for non-adaptive joint source network coding of correlated messages in a Bayesian scenario are studied. Specifically, we introduce one-step Quantized Network Coding (QNC), which is a hybrid combination of network coding and packet forwarding for transmission. Motivated by the work on Bayesian compressed sensing, we derive theoretical guarantees on robust recovery in a one-step QNC scenario. Our mathematical derivations for Gaussian messages express the opportunity of distributed compression by using one-step QNC, as a simplified version of QNC scenario. Our simulation results show an improvement in terms of quality-delay performance over routing based packet forwarding.

Connected through hyperedge HE-1 (induced by source paper "non adaptive distributed compression in networks"):
  with members:
    - "bayesian quantized network coding via belief propagation"
    - "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"
    - "bayesian quantized network coding via belief propagation"
    - "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"

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)