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 "an approximate message passing approach for compressive hyperspectral imaging using a simultaneous low rank and joint sparsity prior"):
  Abstract: This paper considers a compressive sensing (CS) approach for hyperspectral data acquisition, which results in a practical compression ratio substantially higher than the state-of-the-art. Applying simultaneous low-rank and joint-sparse (L&S) model to the hyperspectral data, we propose a novel algorithm to joint reconstruction of hyperspectral data based on loopy belief propagation that enables the exploitation of both structured sparsity and amplitude correlations in the data. Experimental results with real hyperspectral datasets demonstrate that the proposed algorithm outperforms the state-of-the-art CS-based solutions with substantial reductions in reconstruction error.

Includes members:
  Member "hyperspectral unmixing via turbo bilinear approximate message passing":
    also connected through other hyperedges:
      (none)
  Member "bilinear generalized approximate message passing part i derivation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "efficient high dimensional inference in the multiple measurement vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "subspace methods for joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "generalized approximate message passing for estimation with random linear mixing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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)