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: "lipschitz properties for deep convolutional networks"
  Abstract: In this paper we discuss the stability properties of convolutional neural networks. Convolutional neural networks are widely used in machine learning. In classification they are mainly used as feature extractors. Ideally, we expect similar features when the inputs are from the same class. That is, we hope to see a small change in the feature vector with respect to a deformation on the input signal. This can be established mathematically, and the key step is to derive the Lipschitz properties. Further, we establish that the stability results can be extended for more general networks. We give a formula for computing the Lipschitz bound, and compare it with other methods to show it is closer to the optimal value.

Connected through hyperedge HE-1 (induced by source paper "efficient and accurate estimation of lipschitz constants for deep neural networks"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "scaling provable adversarial defenses"
    - "on lipschitz bounds of general convolutional neural networks"
    - "stability certified reinforcement learning a control theoretic perspective"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "sinkhorn autoencoders"):
  with members:
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "implicit reparameterization gradients"
    - "deeppermnet visual permutation learning"
    - "fixing a broken elbo"
    - "representation learning by learning to count"
    - "vae with a vampprior"
    - "deep variational information bottleneck"
    - "tsallis regularized optimal transport and ecological inference"

Connected through hyperedge HE-3 (induced by source paper "lipschitz continuity in model based reinforcement learning"):
  with members:
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "continuous deep q learning with model based acceleration"
    - "self correcting models for model based reinforcement learning"
    - "an alternative softmax operator for reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "on lipschitz bounds of general convolutional neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "improved training of wasserstein gans"
    - "universal adversarial perturbations"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deep convolutional neural networks based on semi discrete frames"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-5 (induced by source paper "concolic testing for deep neural networks"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "global robustness evaluation of deep neural networks with provable guarantees for l0 norm"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "testing deep neural networks"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning with function valued action spaces for partial differential equation control"):
  with members:
    - "modeling information diffusion in online social networks with partial differential equations"
    - "lipschitz continuity in model based reinforcement learning"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "guided policy search as approximate mirror descent"
    - "deep reinforcement learning with attention for slate markov decision processes with high dimensional states and actions"
    - "high dimensional continuous control using generalized advantage estimation"
    - "deep reinforcement learning in large discrete action spaces"
    - "deep reinforcement learning with a natural language action space"

Connected through hyperedge HE-7 (induced by source paper "rendition reclaiming what a black box takes away"):
  with members:
    - "the little engine that could regularization by denoising red"
    - "boosting of image denoising algorithms"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-8 (induced by source paper "energy propagation in deep convolutional neural networks"):
  with members:
    - "deep structured features for semantic segmentation"
    - "deep convolutional neural networks on cartoon functions"
    - "deep residual learning for image recognition"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "rigid motion scattering for texture classification"
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

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)