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: "deep supervised feature selection using stochastic gates"
  Abstract: In this study, we propose a novel non-parametric embedded feature selection method based on minimizing the $\ell_0$ norm of the vector of an indicator variable, whose point-wise product of an input selects a subset of features. Our approach relies on the continuous relaxation of Bernoulli distributions, which allows our model to learn the parameters of the approximate Bernoulli distributions via tractable methods. Using these tools we present a general neural network that simultaneously minimizes a loss function while selecting relevant features. We also provide an information-theoretic justification of incorporating Bernoulli distribution into our approach. Finally, we demonstrate the potential of the approach on synthetic and real-life applications.

Connected through hyperedge HE-1 (induced by source paper "not all features are equal feature leveling deep neural networks for better interpretation"):
  with members:
    - "adversarial patch"
    - "programmatically interpretable reinforcement learning"
    - "verifiable reinforcement learning via policy extraction"
    - "efficient formal safety analysis of neural networks"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "interpretable transformations with encoder decoder networks"
    - "interpretable convolutional neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "sobolev independence criterion"):
  with members:
    - "learning important features through propagating activation differences"
    - "learning visual reasoning without strong priors"
    - "improved training of wasserstein gans"
    - "conditional image generation with pixelcnn decoders"
    - "adam a method for stochastic optimization"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "on integral probability metrics φ divergences and binary classification"

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)