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: "a deep learning approach to structured signal recovery"
  Abstract: In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.

Connected through hyperedge HE-1 (induced by source paper "inference with deep generative priors in high dimensions"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "inverting deep generative models one layer at a time"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "sunlayer stable denoising with generative networks"

Connected through hyperedge HE-2 (induced by source paper "learning space partitions for nearest neighbor search"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "the case for learned index structures"
    - "billion scale similarity search with gpus"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-3 (induced by source paper "deep compressed sensing"):
  with members:
    - "cgans with projection discriminator"
    - "the gan landscape losses architectures regularization and normalization"
    - "the mechanics of n player differentiable games"
    - "spectral normalization for generative adversarial networks"
    - "convcsnet a convolutional compressive sensing framework based on deep learning"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "on unifying deep generative models"

Connected through hyperedge HE-4 (induced by source paper "deep decomposition learning for inverse imaging problems"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "deep image prior"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (induced by source paper "asymptotics of map inference in deep networks"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "inference in deep networks in high dimensions"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-6 (induced by source paper "learning based low rank approximations"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to branch"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "learning combinatorial optimization algorithms over graphs"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "lazysvd even faster svd decomposition yet without agonizing pain"

Connected through hyperedge HE-7 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "compression artifacts removal using convolutional neural networks"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "deepbinarymask learning a binary mask for video compressive sensing"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "learning sublinear time indexing for nearest neighbor search"):
  with members:
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "learning to hash for indexing big data a survey"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.LG(Machine Learning)