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: "block sparse signals uncertainty relations and efficient recovery"
  Abstract: We consider efficient methods for the recovery of block-sparse signals-i.e., sparse signals that have nonzero entries occurring in clusters-from an underdetermined system of linear equations. An uncertainty relation for block-sparse signals is derived, based on a block-coherence measure, which we introduce. We then show that a block-version of the orthogonal matching pursuit algorithm recovers block -sparse signals in no more than steps if the block-coherence is sufficiently small. The same condition on block-coherence is shown to guarantee successful recovery through a mixed -optimization approach. This complements previous recovery results for the block-sparse case which relied on small block-restricted isometry constants. The significance of the results presented in this paper lies in the fact that making explicit use of block-sparsity can provably yield better reconstruction properties than treating the signal as being sparse in the conventional sense, thereby ignoring the additional structure in the problem.

Connected through hyperedge HE-1 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "compression artifacts removal using convolutional neural networks"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "deepbinarymask learning a binary mask for video compressive sensing"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "a new analysis for support recovery with block orthogonal matching pursuit"):
  with members:
    - "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"
    - "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"
    - "nearly optimal bounds for orthogonal least squares"
    - "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant"

Connected through hyperedge HE-3 (induced by source paper "an adaptive bayesian framework for recovery of sources with structured sparsity"):
  with members:
    - "oversampled adaptive sensing with random projections analysis and algorithmic approaches"
    - "theoretical bounds on map estimation in distributed sensing networks"
    - "near optimal adaptive compressed sensing"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-4 (induced by source paper "block sparse recovery of semidefinite systems and generalized null space conditions"):
  with members:
    - "sparse recovery with integrality constraints"
    - "guarantees of total variation minimization for signal recovery"
    - "compressed sensing of block sparse positive vectors"
    - "stable image reconstruction using total variation minimization"

Connected through hyperedge HE-5 (induced by source paper "bayesian topology identification of linear dynamic networks"):
  with members:
    - "prediction error identification of linear dynamic networks with rank reduced noise"
    - "robust em kernel based methods for linear system identification"
    - "topology identification of directed dynamical networks via power spectral analysis"

Connected through hyperedge HE-6 (induced by source paper "the use of mutual coherence to prove ell 1 ell 0 equivalence in classification problems"):
  with members:
    - "a probabilistic and ripless theory of compressed sensing"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-7 (induced by source paper "sparse representation classification via screening for graphs"):
  with members:
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-8 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices"):
  with members:
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "graded quantization for multiple description coding of compressive measurements"
    - "refined analysis of sparse mimo radar"
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "compressed sensing how sharp is the restricted isometry property"

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)