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: "do imagenet classifiers generalize to imagenet"
  Abstract: We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets.

Connected through hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "on mutual information maximization for representation learning"
    - "large scale adversarial representation learning"
    - "classification accuracy score for conditional generative models"
    - "data efficient image recognition with contrastive predictive coding"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "few shot learning with weakly supervised object localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "baby steps towards few shot learning with multiple semantics"
    - "few shot learning for domain specfic fine grained image classfication"
    - "few shot learning via saliency guided hallucination of samples"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "laso label set operations networks for multi label few shot learning"
    - "revisiting metric learning for few shot image classification"

Connected through hyperedge HE-3 (induced by source paper "out of distribution generalization via risk extrapolation rex"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "achieving robustness in the wild via adversarial mixing with disentangled representations"
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a closer look at few shot classification"
    - "a fourier perspective on model robustness in computer vision"
    - "adversarial target invariant representation learning for domain generalization"

Connected through hyperedge HE-4 (induced by source paper "i am going mad maximum discrepancy competition for comparing classifiers adaptively"):
  with members:
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "exploring the limits of weakly supervised pretraining"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "eigen distortions of hierarchical representations"

Connected through hyperedge HE-5 (induced by source paper "al2 progressive activation loss for learning general representations in classification neural networks"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dimensionality driven learning with noisy labels"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "exploring generalization in deep learning"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-6 (induced by source paper "neural kernels without tangents"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "on exact computation with an infinitely wide neural net"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "a kernel theory of modern data augmentation"
    - "gradient descent finds global minima of deep neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"

Connected through hyperedge HE-7 (induced by source paper "what can robotics research learn from computer vision research"):
  with members:
    - "a mask rcnn baseline for probabilistic object detection"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "towards open world recognition"
    - "orb slam a versatile and accurate monocular slam system"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "optimizing jpeg quantization for classification networks"):
  with members:
    - "deepn jpeg a deep neural network favorable jpeg based image compression framework"
    - "understanding how image quality affects deep neural networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

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)