human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the exact recovery condition of simultaneous orthogonal matching pursuit"
  Abstract: Several exact recovery criteria (ERC) ensuring that orthogonal matching pursuit (OMP) identifies the correct support of sparse signals have been developed in the last few years. These ERC rely on the restricted isometry property (RIP), the associated restricted isometry constant (RIC) and sometimes the restricted orthogonality constant (ROC). In this paper, three of the most recent ERC for OMP are examined. The contribution is to show that these ERC remain valid for a generalization of OMP, entitled simultaneous orthogonal matching pursuit (SOMP), that is capable to process several measurement vectors simultaneously and return a common support estimate for the underlying sparse vectors. The sharpness of the bounds is also briefly discussed in light of previous works focusing on OMP.

Connected through hyperedge HE-1 (induced by source paper "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"):
  with members:
    - "on the noise robustness of simultaneous orthogonal matching pursuit"
    - "millimeter wave beam selection using out of band spatial information"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "dynamic subarrays for hybrid precoding in wideband mmwave mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"

Connected through hyperedge HE-2 (induced by source paper "on the noise robustness of simultaneous orthogonal matching pursuit"):
  with members:
    - "improving the correlation lower bound for simultaneous orthogonal matching pursuit"
    - "simultaneous orthogonal matching pursuit with noise stabilization theoretical analysis"
    - "orthogonal matching pursuit a brownian motion analysis"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "joint sparse recovery with semisupervised music"):
  with members:
    - "generalized orthogonal matching pursuit"
    - "subspace methods for joint sparse recovery"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "greedy sparse signal reconstruction using matching pursuit based on hope tree"):
  with members:
    - "generalized orthogonal matching pursuit"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "distributed compressive sensing performance analysis with diverse signal ensembles"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "orthogonal matching pursuit with replacement"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "sparse recovery from combined fusion frame measurements"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal bounds for orthogonal least squares"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "perturbation analysis of orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "improving the correlation lower bound for simultaneous orthogonal matching pursuit"):
  with members:
    - "on the noise robustness of simultaneous orthogonal matching pursuit"
    - "orthogonal matching pursuit with replacement"
    - "distributed compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Question: Please tell me which class the center node 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)