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 sparsity coherence and efficient recovery"
  Abstract: We consider compressed sensing of block-sparse signals, i.e., sparse signals that have nonzero coefficients occuring in clusters. Based on an uncertainty relation for block-sparse signals, we define a block-coherence measure and we show that a block-version of the orthogonal matching pursuit algorithm recovers block k-sparse signals in no more than k steps if the block-coherence is sufficiently small. The same condition on block-sparsity is shown to guarantee successful recovery through a mixed l2/l1 optimization approach. The significance of the results lies in the fact that making explicit use of block-sparsity can 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 "machine learning for geometrically consistent angular spread function estimation in massive mimo"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning methods for improved decoding of linear codes"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "deep learning for joint source channel coding of text"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-2 (induced by source paper "uplink downlink channel covariance transformations and precoding design for fdd massive mimo"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "5g new radio unveiling the essentials of the next generation wireless access technology"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "deep learning for joint source channel coding of text"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"

Connected through hyperedge HE-3 (induced by source paper "gelfand numbers related to structured sparsity and besov space embeddings with small mixed smoothness"):
  with members:
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "uniform recovery of fusion frame structured sparse signals"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-4 (induced by source paper "uniform recovery of fusion frame structured sparse signals"):
  with members:
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "group sparse model selection hardness and relaxations"
    - "recovering low rank matrices from few coefficients in any basis"
    - "sparse recovery from combined fusion frame measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "block length dependent thresholds in block sparse compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "group sparse matrix recovery"):
  with members:
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "structured sparsity through convex optimization"

Connected through hyperedge HE-6 (induced by source paper "solving oscar regularization problems by proximal splitting algorithms"):
  with members:
    - "structured sparsity through convex optimization"
    - "fast overlapping group lasso"
    - "network flow algorithms for structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "peak reduction and clipping mitigation in ofdm by augmented compressive sensing"):
  with members:
    - "general deviants an analysis of perturbations in compressed sensing"
    - "a probabilistic and ripless theory of compressed sensing"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-8 (induced by source paper "sparse recovery from combined fusion frame measurements"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "model based compressive sensing"
    - "circulant and toeplitz matrices in compressed sensing"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

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)