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: "distributed compressive sensing a deep learning approach"
  Abstract: Several recent studies on the compressed sensing problem with Multiple Measurement Vectors (MMVs) under the condition that the vectors in the different channels are jointly sparse have been recently carried. In this paper, this condition is relaxed. Instead, these sparse vectors are assumed to depend on each other but this dependency is assumed unknown. We capture this dependency by computing the conditional probability of each entry in each vector being non-zero, given the “ residuals ” of all previous vectors. To estimate these probabilities, we propose the use of the long short-term memory (LSTM), a data-driven model for sequence modeling that is deep in time. To learn the model parameters, we minimize a cross-entropy cost function. To reconstruct the sparse vectors at the decoder, we propose a greedy solver that uses the above model to estimate the conditional probabilities. By performing extensive experiments on two real world datasets, we show that the proposed method significantly outperforms the general MMV solver (the Simultaneous Orthogonal Matching Pursuit (SOMP)) and a number of the model-based Bayesian methods. The proposed method does not add any complexity to the general compressive sensing encoder. The trained model is used at the decoder only. As the proposed method is a data-driven method, it is only applicable when training data is available. In many applications however, training data is indeed available, e.g., in recorded images for which our method is successfully applied as to be reported in this paper.

Connected through hyperedge HE-1 (induced by source paper "tree search network for sparse regression"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep sparse coding using optimized linear expansion of thresholds"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "adaptive feature selection computationally efficient online sparse linear regression under rip"
    - "admm net a deep learning approach for compressive sensing mri"
    - "greedy subspace pursuit for joint sparse recovery"

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.LG(Machine Learning)