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 "belief propagation for joint sparse recovery"):
  Abstract: Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple signals share the same common sparse support sets, and they are measured through the same sensing matrix. Leveraging a recent information theoretic characterization of single signal CS, we formulate the optimal minimum mean square error (MMSE) estimation problem, and derive a belief propagation algorithm, its relaxed version, for the joint sparse recovery problem and an approximate message passing algorithm. In addition, using density evolution, we provide a sufficient condition for exact recovery.

Includes members:
  Member "asymptotic analysis of map estimation via the replica method and applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "subspace methods for joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "compressive music a missing link between compressive sensing and array signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a compact formulation for the ell_ 2 1 mixed norm minimization problem")
  Member "estimation with random linear mixing belief propagation and compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "bayesian compressive sensing via belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")

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)