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 "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.

Includes members:
  Member "quantized network coding for sparse messages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "match made in heaven practical compressed sensing and network coding for intelligent distributed communication networks")
  Member "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "serial quantization for sparse time sequences")
  Member "improved bounds on restricted isometry constants for gaussian matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "compressive sensing over networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "phase transitions for greedy sparse approximation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "network information flow with correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity bounds for networks with correlated sources and characterisation of distributions by entropies")

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)