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: "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"
  Abstract: We propose a retrieval-augmented convolutional network and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid architecture combining a convolutional network and an off-the-shelf retrieval engine was designed to mitigate the adverse effect of off-manifold adversarial examples, while the proposed local mixup addresses on-manifold ones by explicitly encouraging the classifier to locally behave linearly on the data manifold. Our evaluation of the proposed approach against five readily-available adversarial attacks on three datasets--CIFAR-10, SVHN and ImageNet--demonstrate the improved robustness compared to the vanilla convolutional network.

Connected through hyperedge HE-1 (induced by source paper "embedding propagation smoother manifold for few shot classification"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "charting the right manifold manifold mixup for few shot learning"
    - "meta learning with implicit gradients"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "few shot learning with metric agnostic conditional embeddings"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-2 (induced by source paper "defense against adversarial images using web scale nearest neighbor search"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "exploring the limits of weakly supervised pretraining"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "generalization through memorization nearest neighbor language models"):
  with members:
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "dynamic evaluation of transformer language models"
    - "improving neural language models by segmenting attending and predicting the future"
    - "defending against neural fake news"
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "retrieve and refine improved sequence generation models for dialogue"

Connected through hyperedge HE-4 (induced by source paper "improving the robustness of imagenet classifiers using elements of human visual cognition"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "interpreting adversarially trained convolutional neural networks"
    - "humans can decipher adversarial images"
    - "a simple cache model for image recognition"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "generalisation in humans and deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-5 (induced by source paper "adversarial examples in deep learning characterization and divergence"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "blind pre processing a robust defense method against adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "robust pre processing a robust defense method against adversary attack"
    - "sok security and privacy in machine learning"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-6 (induced by source paper "a simple cache model for image recognition"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "mixup beyond empirical risk minimization"
    - "neural episodic control"
    - "learning to remember rare events"
    - "unbounded cache model for online language modeling with open vocabulary"
    - "identity mappings in deep residual networks"

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)