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 "distributed compressive sensing performance analysis with diverse signal ensembles"):
  Abstract: Distributed compressive sensing is a framework considering jointly sparsity within signal ensembles along with multiple measurement vectors (MMVs). The current theoretical bound of performance for MMVs, however, is derived to be the same with that for single MV (SMV) because no assumption about signal ensembles is made. #R##N#In this work, we propose a new concept of inducing the factor called "Euclidean distances between signals" for the performance analysis of MMVs. The novelty is that the size of signal ensembles will be taken into consideration in our analysis to theoretically show that MMVs indeed exhibit better performance than SMV. Although our concept can be broadly applied to CS algorithms with MMVs, the case study conducted on a well-known greedy solver called simultaneous orthogonal matching pursuit (SOMP) will be explored in this paper. We show that the performance of SOMP, when incorporated with our concept by modifying the steps of support detection and signal estimations, will be improved remarkably, especially when the Euclidean distances between signals are short. The performance of modified SOMP is verified to meet our theoretical prediction.

Includes members:
  Member "on the exact recovery condition of simultaneous orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach")
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection")
  Member "measurement bounds for sparse signal ensembles via graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "orthogonal matching pursuit with replacement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noisy subspace clustering via matching pursuits")
  Member "support recovery of sparse signals in the presence of multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint sparse recovery using signal space matching pursuit")
  Member "sparse recovery from combined fusion frame measurements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse recovery of fusion frame structured signals")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")

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)