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 "a new analysis for support recovery with block orthogonal matching pursuit"):
  Abstract: Compressed sensing is a signal processing technique, which can accurately recover sparse signals from linear measurements with far fewer number of measurements than those required by the classical Shannon–Nyquist theorem. Block sparse signals, i.e., the sparse signals whose nonzero coefficients occur in few blocks, arise from many fields. Block orthogonal matching pursuit (BOMP) is a popular greedy algorithm for recovering block sparse signals due to its high efficiency and effectiveness. By fully using the block sparsity of block sparse signals, BOMP can achieve very good recovery performance. This letter proposes a sufficient condition to ensure that BOMP can exactly recover the support of block   $K$  -sparse signals under the noisy case. This condition is better than existing ones.

Includes members:
  Member "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit")
  Member "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit")
  Member "nearly optimal bounds for orthogonal least squares":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an optimal condition of robust low rank matrices recovery")
  Member "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "alternating direction algorithms for ell_0 regularization in compressed sensing")
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing")

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)