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: "classification regions of deep neural networks"
  Abstract: The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. Through a systematic empirical investigation, we show that state-of-the-art deep nets learn connected classification regions, and that the decision boundary in the vicinity of datapoints is flat along most directions. We further draw an essential connection between two seemingly unrelated properties of deep networks: their sensitivity to additive perturbations in the inputs, and the curvature of their decision boundary. The directions where the decision boundary is curved in fact remarkably characterize the directions to which the classifier is the most vulnerable. We finally leverage a fundamental asymmetry in the curvature of the decision boundary of deep nets, and propose a method to discriminate between original images, and images perturbed with small adversarial examples. We show the effectiveness of this purely geometric approach for detecting small adversarial perturbations in images, and for recovering the labels of perturbed images.

Connected through hyperedge HE-1 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study"):
  with members:
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "cross entropy loss and low rank features have responsibility for adversarial examples"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the attack generator a systematic approach towards constructing adversarial attacks"
    - "deep skinny neural networks are not universal approximators"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-2 (induced by source paper "on the resilience of biometric authentication systems against random inputs"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "explainable black box attacks against model based authentication"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "voxceleb2 deep speaker recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "voxceleb a large scale speaker identification dataset"
    - "tacotron towards end to end speech synthesis"
    - "mitigating evasion attacks to deep neural networks via region based classification"

Connected through hyperedge HE-3 (induced by source paper "defending against universal attacks through selective feature regeneration"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "deepcorrect correcting dnn models against image distortions"
    - "playing the game of universal adversarial perturbations"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"

Connected through hyperedge HE-4 (induced by source paper "the local elasticity of neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "implicit regularization in deep matrix factorization"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "stiffness a new perspective on generalization in neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"

Connected through hyperedge HE-5 (induced by source paper "one pixel attack for fooling deep neural networks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "self organizing classifiers first steps in structured evolutionary machine learning"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "self organizing classifiers and niched fitness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "exploring the landscape of spatial robustness"

Connected through hyperedge HE-6 (induced by source paper "making targeted black box evasion attacks effective and efficient"):
  with members:
    - "copy and paste a simple but effective initialization method for black box adversarial attacks"
    - "reducing adversarial example transferability using gradient regularization"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "model reuse attacks on deep learning systems"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "query efficient black box attack by active learning"
    - "prada protecting against dnn model stealing attacks"
    - "genattack practical black box attacks with gradient free optimization"

Connected through hyperedge HE-7 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "defending against adversarial examples with k nearest neighbor"
    - "on evaluating adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "dnn or k nn that is the generalize vs memorize question"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "the tale of evil twins adversarial inputs versus backdoored models"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "model reuse attacks on deep learning systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adef an iterative algorithm to construct adversarial deformations"
    - "spectral signatures in backdoor attacks"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "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.CV(Computer Vision and Pattern Recognition)