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: "adversarial examples are a natural consequence of test error in noise"
  Abstract: Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to small modifications of a correctly handled input. Less surprisingly, image classifiers also lack human-level performance on randomly corrupted images, such as images with additive Gaussian noise. In this paper we provide both empirical and theoretical evidence that these are two manifestations of the same underlying phenomenon, establishing close connections between the adversarial robustness and corruption robustness research programs. This suggests that improving adversarial robustness should go hand in hand with improving performance in the presence of more general and realistic image corruptions. Based on our results we recommend that future adversarial defenses consider evaluating the robustness of their methods to distributional shift with benchmarks such as Imagenet-C.

Connected through hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"
    - "randaugment practical automated data augmentation with a reduced search space"

Connected through hyperedge HE-2 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "certified adversarial robustness via randomized smoothing"
    - "transfer of adversarial robustness between perturbation types"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"

Connected through hyperedge HE-3 (induced by source paper "utilizing network properties to detect erroneous inputs"):
  with members:
    - "looking ahead anticipating pedestrians crossing with future frames prediction"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "natural adversarial examples"
    - "selectivenet a deep neural network with an integrated reject option"
    - "improved adversarial robustness by reducing open space risk via tent activations"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-4 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models"):
  with members:
    - "using videos to evaluate image model robustness"
    - "seeing what a gan cannot generate"
    - "natural adversarial examples"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "interpreting and evaluating neural network robustness"

Connected through hyperedge HE-5 (induced by source paper "adversarial data encryption"):
  with members:
    - "convolutional networks with dense connectivity"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial music real world audio adversary against wake word detection system"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-6 (induced by source paper "g odel s sentence is an adversarial example but unsolvable"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "disentangling adversarial robustness and generalization"

Connected through hyperedge HE-7 (induced by source paper "on the robustness of face recognition algorithms against attacks and bias"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "deep learning for face recognition pride or prejudiced"
    - "on detecting gans and retouching based synthetic alterations"
    - "on evaluating adversarial robustness"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "fast geometrically perturbed adversarial faces"
    - "recognizing disguised faces in the wild"

Connected through hyperedge HE-8 (induced by source paper "heat and blur an effective and fast defense against adversarial examples"):
  with members:
    - "concise explanations of neural networks using adversarial training"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "saliency methods for explaining adversarial attacks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "spatially transformed adversarial examples"
    - "interpretable deep learning under fire"
    - "concise and stable explanations using adversarial training"
    - "feature squeezing detecting adversarial examples in deep neural 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)