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 "deep one bit compressive autoencoding"):
  Abstract: Parameterized mathematical models play a central role in understanding and design of complex information systems. However, they often cannot take into account the intricate interactions innate to such systems. On the contrary, purely data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of interpretability. In this paper, we consider the design of a one-bit compressive autoencoder, and propose a novel hybrid model-based and data-driven methodology that allows us to not only design the sensing matrix for one-bit data acquisition, but also allows for learning the latent-parameters of an iterative optimization algorithm specifically designed for the problem of one-bit sparse signal recovery. Our results demonstrate a significant improvement compared to state-of-the-art model-based algorithms.

Includes members:
  Member "signal recovery from 1 bit quantized noisy samples via adaptive thresholding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "comprehensive personalized ranking using one bit comparison data")
  Member "a comparison of hybrid beamforming and digital beamforming with low resolution adcs for multiple users and imperfect csi":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advanced receiver architectures for millimeter wave communications with low resolution adcs")
  Member "deep fully connected networks for video compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive image sampling using deep learning and its application on x ray fluorescence image reconstruction")
  Member "reconnet non iterative reconstruction of images from compressively sensed random measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "a deep learning approach to structured signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep unfolding model based inspiration of novel deep architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)