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: "onsager corrected deep learning for sparse linear inverse problems"
  Abstract: Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem encountered in compressive sensing, where one seeks to recover a sparse signal from a small number of noisy linear measurements. In this paper, we propose a novel neural-network architecture that decouples prediction errors across layers in the same way that the approximate message passing (AMP) algorithm decouples them across iterations: through Onsager correction. Numerical experiments suggest that our "learned AMP" network significantly improves upon Gregor and LeCun's "learned ISTA" network in both accuracy and complexity.

Connected through hyperedge HE-1 (induced by source paper "learning to detect"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "learning approximate neural estimators for wireless channel state information"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (induced by source paper "trainable ista for sparse signal recovery"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "learning to decode linear codes using deep learning"
    - "a survey of sparse representation algorithms and applications"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"

Connected through hyperedge HE-3 (induced by source paper "deep learning assisted user identification in massive machine type communications"):
  with members:
    - "on the performance gain of noma over oma in uplink communication systems"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "deep learning for passive synthetic aperture radar"):
  with members:
    - "deep convolutional neural network for inverse problems in imaging"
    - "recurrent inference machines for solving inverse problems"
    - "learning to learn by gradient descent by gradient descent"
    - "a deep learning approach to block based compressed sensing of images"
    - "a deep learning approach to structured signal recovery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "lsalsa accelerated source separation via learned sparse coding"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "skip connections eliminate singularities"
    - "deep recurrent nmf for speech separation by unfolding iterative thresholding"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "text flow a unified text detection system in natural scene images"
    - "highway and residual networks learn unrolled iterative estimation"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "fast inference in sparse coding algorithms with applications to object recognition"

Connected through hyperedge HE-6 (induced by source paper "theoretical linear convergence of unfolded ista and its practical weights and thresholds"):
  with members:
    - "maximal sparsity with deep networks"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"

Connected through hyperedge HE-7 (induced by source paper "amp inspired deep networks for sparse linear inverse problems"):
  with members:
    - "multi layer generalized linear estimation"
    - "hybrid approximate message passing"
    - "deep fully connected networks for video compressive sensing"
    - "learning and free energies for vector approximate message passing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "vector approximate message passing for the generalized linear model"

Connected through hyperedge HE-8 (induced by source paper "an introduction to deep learning for the physical layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "learning to protect communications with adversarial neural cryptography"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "on deep learning based channel decoding"
    - "unsupervised representation learning of structured radio communication signals"
    - "reinforcement learning with unsupervised auxiliary tasks"

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)