human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "quantized network coding for correlated sources"):
  Abstract: Non-adaptive joint source network coding of correlated sources is discussed in this paper. By studying the information flow in the network, we propose quantized network coding as an alternative for packet forwarding. This technique has both network coding and distributed source coding advantages, simultaneously. Quantized network coding is a combination of random linear network coding in the (infinite) field of real numbers and quantization to cope with the limited capacity of links. With the aid of the results in the literature of compressed sensing, we discuss theoretical and practical feasibility of quantized network coding in lossless networks. We show that, due to the nature of the field it operates on, quantized network coding can provide good quality decoding at a sink node with the reception of a reduced number of packets. Specifically, we discuss the required conditions on local network coding coefficients, by using restricted isometry property and suggest a design, which yields in appropriate linear measurements. Finally, our simulation results show the achieved gain in terms of delivery delay, compared to conventional routing based packet forwarding.

Includes members:
  Member "non adaptive distributed compression in networks":
    also connected through other hyperedges:
      (none)
  Member "bayesian quantized network coding via belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non adaptive distributed compression in networks")
  Member "one step quantized network coding for near sparse gaussian messages":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non adaptive distributed compression in networks")
  Member "quantized network coding for sparse messages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "match made in heaven practical compressed sensing and network coding for intelligent distributed communication networks")
  Member "restricted isometry property in quantized network coding of sparse messages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust bayesian compressed sensing over finite fields asymptotic performance analysis")
  Member "sparse recovery with graph constraints fundamental limits and measurement construction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive closeness in networks")
  Member "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "serial quantization for sparse time sequences")
  Member "compressive sensing over graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural network tomography")

Question: Please tell me which class the source paper 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)