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 "inferring sparsity compressed sensing using generalized restricted boltzmann machines"):
  Abstract: In this work, we consider compressed sensing reconstruction from M measurements of K-sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how this signal model can be used within a Bayesian framework of signal reconstruction. By deriving a message-passing inference for general distribution restricted Boltzmann machines, we are able to integrate these inferred signal models into approximate message passing for compressed sensing reconstruction. Finally, we show for the MNIST dataset that this approach can be very effective, even for M < K.

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 "reconnet non iterative reconstruction of images from compressively sensed random measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "approximate message passing with restricted boltzmann machine priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tree search network for sparse regression")
  Member "learning optimal nonlinearities for iterative thresholding algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "a survey of sparse representation algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "a deep learning approach to structured signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "hybrid approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "random access in c ran for user activity detection with limited capacity fronthaul")

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)