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 "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.

Includes members:
  Member "hybrid approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "generalized approximate message passing for cosparse analysis compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beamspace channel estimation in mmwave systems via cosparse image reconstruction technique")
  Member "expectation maximization gaussian mixture approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "hybrid approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "hybrid approximate message passing with applications to structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "random access in c ran for user activity detection with limited capacity fronthaul")
  Member "generalized approximate message passing for estimation with random linear mixing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "message passing algorithms for compressed sensing i motivation and construction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random access for massive machine type communications")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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.IT(Information Theory)