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 "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"):
  Abstract: This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the sparsity pattern of signals belonging to the same class. The determined probability distribution is then used in a maximum  a posteriori  approach for the reconstruction. The parameters of the prior distribution are learned from training data. The motivation behind this approach is to model the higher–order statistical dependencies between the coefficients of the sparse representation, with the final goal of improving the reconstruction. The performance of the proposed method is validated on the Berkeley Segmentation Dataset and the MNIST Database of handwritten digits.

Includes members:
  Member "approximate message passing with restricted boltzmann machine priors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tree search network for sparse regression")
  Member "sparse approximation based on a random overcomplete basis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross validation in lasso and its acceleration")
  Member "variational bayesian algorithm for quantized compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "cramer rao type bounds for sparse bayesian learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information geometric approach to bayesian lower error bounds")
  Member "bayesian fusion of multi band images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inversenet solving inverse problems with splitting networks")
  Member "exploiting statistical dependencies in sparse representations for signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi focus image fusion via coupled sparse representation and dictionary learning")
  Member "structured compressed sensing from theory to applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.LG(Machine Learning)