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: "approximate message passing with restricted boltzmann machine priors"
  Abstract: Approximate  message  passing  (AMP)  has  been  shown  to  be  an  excellent  statistical  approach  to  signal  inference  and  compressed sensing  problems.  The  AMP  framework  provides  modularity  in  the  choice  of  signal  prior; here we propose a hierarchical form of the Gauss Bernoulli prior which utilizes a restricted Boltzmann machine (RBM) trained on the signal support to  push  reconstruction  performance  beyond  that  of  simple  i.i.d. priors  for  signals whose support can be well represented by a trained binary RBM. We present and analyze two methods of RBM factorization and demonstrate how these affect signal reconstruction performance within our proposed algorithm. Finally, using the MNIST handwritten digit dataset, we show experimentally that using an RBM allows AMP to approach oracle-support performance.

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"
    - "distributed compressive sensing a deep learning approach"

Connected through hyperedge HE-2 (induced by source paper "deterministic and generalized framework for unsupervised learning with restricted boltzmann machines"):
  with members:
    - "gaussian binary restricted boltzmann machines for modeling natural image statistics"

Connected through hyperedge HE-3 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "state evolution for approximate message passing with non separable functions"):
  with members:
    - "near optimal matrix recovery from random linear measurements"
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "hybrid approximate message passing"
    - "dynamical functional theory for compressed sensing"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "compressive phase retrieval via generalized approximate message passing"
    - "sharp time data tradeoffs for linear inverse problems"

Connected through hyperedge HE-5 (induced by source paper "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"):
  with members:
    - "sparse approximation based on a random overcomplete basis"
    - "variational bayesian algorithm for quantized compressed sensing"
    - "cramer rao type bounds for sparse bayesian learning"
    - "bayesian fusion of multi band images"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-6 (induced by source paper "inferring sparsity compressed sensing using generalized restricted boltzmann machines"):
  with members:
    - "hybrid approximate message passing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "a survey of sparse representation algorithms and applications"
    - "a deep learning approach to structured signal recovery"
    - "hybrid approximate message passing"
    - "hybrid approximate message passing with applications to structured sparsity"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-7 (induced by source paper "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"
    - "compressed sensing of approximately sparse signals phase transitions and optimal reconstruction"
    - "phase retrieval via matrix completion"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

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)