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: "eigen distortions of hierarchical representations"
  Abstract: We develop a method for comparing hierarchical image representations in terms of their ability to explain perceptual sensitivity in humans. Specifically, we utilize Fisher information to establish a model-derived prediction of sensitivity to local perturbations of an image. For a given image, we compute the eigenvectors of the Fisher information matrix with largest and smallest eigenvalues, corresponding to the model-predicted most- and least-noticeable image distortions, respectively. For human subjects, we then measure the amount of each distortion that can be reliably detected when added to the image, and compare these thresholds to the predictions of the corresponding model. We use this method to test the ability of a variety of representations to mimic human perceptual sensitivity. We find that the early layers of VGG16, a deep neural network optimized for object recognition, provide a better match to human perception than later layers, and a better match than a 4-stage convolutional neural network (CNN) trained on a database of human ratings of distorted image quality. On the other hand, we find that simple models of early visual processing, incorporating one or more stages of local gain control, trained on the same database of distortion ratings, provide substantially better predictions of human sensitivity than both the CNN and all layers of VGG16.

Connected through hyperedge HE-1 (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"
    - "do imagenet classifiers generalize to imagenet"
    - "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"

Connected through hyperedge HE-2 (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-3 (induced by source paper "e lpips robust perceptual image similarity via random transformation ensembles"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "pixel recursive super resolution"
    - "synthesizing robust adversarial examples"
    - "towards robust neural networks via random self ensemble"

Connected through hyperedge HE-4 (induced by source paper "towards metamerism via foveated style transfer"):
  with members:
    - "sideeye a generative neural network based simulator of human peripheral vision"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "object detection through search with a foveated visual system"
    - "perceptually optimized image rendering"
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"

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)