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: "peernets exploiting peer wisdom against adversarial attacks"
  Abstract: Deep learning systems have become ubiquitous in many aspects of our lives. Unfortunately, it has been shown that such systems are vulnerable to adversarial attacks, making them prone to potential unlawful uses. Designing deep neural networks that are robust to adversarial attacks is a fundamental step in making such systems safer and deployable in a broader variety of applications (e.g. autonomous driving), but more importantly is a necessary step to design novel and more advanced architectures built on new computational paradigms rather than marginally building on the existing ones. In this paper we introduce PeerNets, a novel family of convolutional networks alternating classical Euclidean convolutions with graph convolutions to harness information from a graph of peer samples. This results in a form of non-local forward propagation in the model, where latent features are conditioned on the global structure induced by the graph, that is up to 3 times more robust to a variety of white- and black-box adversarial attacks compared to conventional architectures with almost no drop in accuracy.

Connected through hyperedge HE-1 (induced by source paper "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretical evidence for adversarial robustness through randomization"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-2 (induced by source paper "conditional neural style transfer with peer regularized feature transform"):
  with members:
    - "few shot unsupervised image to image translation"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "the relativistic discriminator a key element missing from standard gan"
    - "a style aware content loss for real time hd style transfer"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "avatar net multi scale zero shot style transfer by feature decoration"

Connected through hyperedge HE-3 (induced by source paper "graph neural based end to end data association framework for online multiple object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "relational deep reinforcement learning"
    - "features for multi target multi camera tracking and re identification"
    - "a twofold siamese network for real time object tracking"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "model free tracking with deep appearance and motion features integration"
    - "relational inductive biases deep learning and graph networks"
    - "semi supervised learning on graphs with generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "fake news detection on social media using geometric deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "on the convergence of adam and beyond"
    - "dual primal graph convolutional networks"
    - "fake news a survey of research detection methods and opportunities"
    - "dynamic graph cnn for learning on point clouds"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "self normalizing neural networks"

Connected through hyperedge HE-5 (induced by source paper "residual attention graph convolutional network for geometric 3d scene classification"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "open3d a modern library for 3d data processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "dynamic graph cnn for learning on point clouds"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "segcloud semantic segmentation of 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "deep geometric knowledge distillation with graphs"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "triplet distillation for deep face recognition"
    - "relational knowledge distillation"
    - "attention based pruning for shift networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning to prune filters in convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"

Connected through hyperedge HE-7 (induced by source paper "patch level neighborhood interpolation a general and effective graph based regularization strategy"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "mixmatch a holistic approach to semi supervised learning"
    - "tangent normal adversarial regularization for semi supervised learning"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "semi supervised learning via compact latent space clustering"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "triple generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "introducing graph smoothness loss for training deep learning architectures"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "in defense of the triplet loss for person re identification"
    - "mixup beyond empirical risk minimization"
    - "regularizing neural networks by penalizing confident output distributions"
    - "identity mappings in deep residual networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "analysis of classifiers robustness to adversarial perturbations"

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)