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: "quantized network coding for sparse messages"
  Abstract: In this paper, we study the data gathering problem in the context of power grids by using a network of sensors, where the sensed data have inter-node redundancy. Specifically, we propose a new transmission method, called quantized network coding, which performs linear network coding in the infinite field of real numbers, and quantization to accommodate the finite capacity of edges. By using the concepts in compressed sensing literature, we propose to use l 1 -minimization to decode the quantized network coded packets, especially when the number of received packets at the decoder is less than the size of sensed data (i.e. number of nodes). We also propose an appropriate design for network coding coefficients, based on restricted isometry property, which results in robust l 1 -min decoding. Our numerical analysis show that the proposed quantized network coding scheme with l 1 -min decoding can achieve significant improvements, in terms of compression ratio and delivery delay, compared to conventional packet forwarding.

Connected through hyperedge HE-1 (induced by source paper "match made in heaven practical compressed sensing and network coding for intelligent distributed communication networks"):
  with members:
    - "probability of partially decoding network coded messages"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"

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"
    - "restricted isometry property in quantized network coding of sparse messages"
    - "compressive sensing over networks"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (induced by source paper "reconstruction of network coded sources from incomplete datasets"):
  with members:
    - "approximate decoding approaches for network coded correlated data"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "restricted isometry property in quantized network coding of sparse messages"):
  with members:
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"
    - "compressive sensing over networks"
    - "phase transitions for greedy sparse approximation algorithms"
    - "network information flow with correlated sources"

Connected through hyperedge HE-6 (induced by source paper "bayesian quantized network coding via belief propagation"):
  with members:
    - "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"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-7 (induced by source paper "one step quantized network coding for near sparse gaussian messages"):
  with members:
    - "bayesian quantized network coding via belief propagation"
    - "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)