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: "on the limitation of convolutional neural networks in recognizing negative images"
  Abstract: Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance on a variety of computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. In this paper, we examine whether CNNs are capable of learning the semantics of training data. To this end, we evaluate CNNs on negative images, since they share the same structure and semantics as regular images and humans can classify them correctly. Our experimental results indicate that when training on regular images and testing on negative images, the model accuracy is significantly lower than when it is tested on regular images. This leads us to the conjecture that current training methods do not effectively train models to generalize the concepts. We then introduce the notion of semantic adversarial examples - transformed inputs that semantically represent the same objects, but the model does not classify them correctly - and present negative images as one class of such inputs.

Connected through hyperedge HE-1 (induced by source paper "metaadvdet towards robust detection of evolving adversarial attacks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "spatially transformed adversarial examples"
    - "detecting adversarial examples via neural fingerprinting"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "rethinking feature distribution for loss functions in image classification"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-2 (induced by source paper "functional adversarial attacks"):
  with members:
    - "transfer of adversarial robustness between perturbation types"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-3 (induced by source paper "mute data similarity driven multi hot target encoding for neural network design"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "beyond one hot encoding lower dimensional target embedding"
    - "datasheets for datasets"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "label embedding for image classification"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "black box adversarial attacks with limited queries and information"):
  with members:
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "black box attacks against rnn based malware detection algorithms"
    - "robust physical world attacks on machine learning models"
    - "simple black box adversarial perturbations for deep networks"
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"

Connected through hyperedge HE-5 (induced by source paper "assessing shape bias property of convolutional neural networks"):
  with members:
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "how intelligent are convolutional neural networks"
    - "exploring generalization in deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "classification by re generation towards classification based on variational inference"):
  with members:
    - "adam a method for stochastic optimization"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "semi supervised learning with deep generative models"

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)