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: "measuring the tendency of cnns to learn surface statistical regularities"
  Abstract: Deep CNNs are known to exhibit the following peculiarity: on the one hand they generalize extremely well to a test set, while on the other hand they are extremely sensitive to so-called adversarial perturbations. The extreme sensitivity of high performance CNNs to adversarial examples casts serious doubt that these networks are learning high level abstractions in the dataset. We are concerned with the following question: How can a deep CNN that does not learn any high level semantics of the dataset manage to generalize so well? The goal of this article is to measure the tendency of CNNs to learn surface statistical regularities of the dataset. To this end, we use Fourier filtering to construct datasets which share the exact same high level abstractions but exhibit qualitatively different surface statistical regularities. For the SVHN and CIFAR-10 datasets, we present two Fourier filtered variants: a low frequency variant and a randomly filtered variant. Each of the Fourier filtering schemes is tuned to preserve the recognizability of the objects. Our main finding is that CNNs exhibit a tendency to latch onto the Fourier image statistics of the training dataset, sometimes exhibiting up to a 28% generalization gap across the various test sets. Moreover, we observe that significantly increasing the depth of a network has a very marginal impact on closing the aforementioned generalization gap. Thus we provide quantitative evidence supporting the hypothesis that deep CNNs tend to learn surface statistical regularities in the dataset rather than higher-level abstract concepts.

Connected through hyperedge HE-1 (induced by source paper "an explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "implicit autoencoders"
    - "explicit disentanglement of appearance and perspective in generative models"
    - "an explicitly relational neural network architecture"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent representations in neural networks for clustering through pseudo supervision and graph based activity regularization"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "fundamental tradeoffs between invariance and sensitivity to adversarial perturbations"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "adversarial perceptual ad blocking meets adversarial machine learning"
    - "testing robustness against unforeseen adversaries"
    - "a fourier perspective on model robustness in computer vision"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

Connected through hyperedge HE-3 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "online normalization for training neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "when does label smoothing help"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "stand alone self attention in vision models"
    - "are perceptually aligned gradients a general property of robust classifiers"

Connected through hyperedge HE-4 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-5 (induced by source paper "v prom a benchmark for visual reasoning using visual progressive matrices"):
  with members:
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"
    - "learning generalized reactive policies using deep neural networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "an analysis of visual question answering algorithms"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "toyarchitecture unsupervised learning of interpretable models of the world"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a neurally inspired hierarchical prediction network for spatiotemporal sequence learning and prediction"
    - "unsupervised predictive memory in a goal directed agent"
    - "the consciousness prior"
    - "a simple neural network module for relational reasoning"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "dynamic routing between capsules"
    - "neural symbolic learning and reasoning a survey and interpretation"

Connected through hyperedge HE-7 (induced by source paper "interpreting adversarially trained convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "theoretically principled trade off between robustness and accuracy"
    - "spatially transformed adversarial examples"
    - "feature denoising for improving adversarial robustness"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-8 (induced by source paper "learning robust global representations by penalizing local predictive power"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "do imagenet classifiers generalize to imagenet"
    - "best sources forward domain generalization through source specific nets"
    - "domain invariant projection learning for zero shot recognition"
    - "deep visual domain adaptation a survey"

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)