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: "restricted isometry property in quantized network coding of sparse messages"
  Abstract: In this paper, we study joint network coding and distributed source coding of inter-node dependent messages, with the perspective of compressed sensing. Specifically, the theoretical guarantees for robust $\ell_1$-min recovery of an under-determined set of linear network coded sparse messages are investigated. We discuss the guarantees for $\ell_1$-min decoding of quantized network coded messages, using the proposed local network coding coefficients in \cite{naba}, based on Restricted Isometry Property (RIP) of the resulting measurement matrix. Moreover, the relation between tail probability of $\ell_2$-norms and satisfaction of RIP is derived and used to compare our designed measurement matrix, with i.i.d. Gaussian measurement matrix. Finally, we present our numerical evaluations, which shows that the proposed design of network coding coefficients result in a measurement matrix with an RIP behavior, similar to that of i.i.d. Gaussian matrix.

Connected through hyperedge HE-1 (induced by source paper "robust bayesian compressed sensing over finite fields asymptotic performance analysis"):
  with members:
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "non adaptive distributed compression in networks"):
  with members:
    - "bayesian quantized network coding via belief propagation"
    - "one step quantized network coding for near sparse gaussian messages"
    - "quantized network coding for sparse messages"
    - "compressive sensing over networks"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (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"
    - "one step quantized network coding for near sparse gaussian messages"
    - "quantized network coding for 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-4 (induced by source paper "bayesian quantized network coding via belief propagation"):
  with members:
    - "quantized network coding for sparse messages"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-5 (induced by source paper "one step quantized network coding for near sparse gaussian messages"):
  with members:
    - "bayesian quantized network coding via belief propagation"
    - "quantized network coding for 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)