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: "can the early human visual system compete with deep neural networks"
  Abstract: We study and compare the human visual system and state-of-the-art deep neural networks on classification of distorted images. Different from previous works, we limit the display time to 100ms to test only the early mechanisms of the human visual system, without allowing time for any eye movements or other higher level processes. Our findings show that the human visual system still outperforms modern deep neural networks under blurry and noisy images. These findings motivate future research into developing more robust deep networks.

Connected through hyperedge HE-1 (induced by source paper "angular visual hardness"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "confidence regularized self training"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "decoupled networks"
    - "reducing overconfident errors outside the known distribution"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-2 (induced by source paper "toward automatic comparison of visualization techniques application to graph visualization"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "bigdl a distributed deep learning framework for big data"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "xception deep learning with depthwise separable convolutions"
    - "25 years of cnns can we compare to human abstraction capabilities"
    - "learning to count with deep object features"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "generalisation in humans and deep neural networks"):
  with members:
    - "totally looks like how humans compare compared to machines"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "exploring generalization in deep learning"
    - "human perception in computer vision"
    - "on classification of distorted images with deep convolutional neural networks"
    - "opening the black box of deep neural networks via information"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-4 (induced by source paper "cifar10 to compare visual recognition performance between deep neural networks and humans"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"
    - "aggregated residual transformations for deep neural networks"
    - "wide residual networks"
    - "deep networks can resemble human feed forward vision in invariant object recognition"

Connected through hyperedge HE-5 (induced by source paper "dissociable neural representations of adversarially perturbed images in deep neural networks and the human brain"):
  with members:
    - "generalisation in humans and deep neural networks"
    - "deep learning a critical appraisal"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "analysis of universal adversarial perturbations"
    - "adversarial examples attacks and defenses for deep learning"
    - "robustness of classifiers from adversarial to random noise"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-6 (induced by source paper "generative sensing transforming unreliable sensor data for reliable recognition"):
  with members:
    - "deepcorrect correcting dnn models against image distortions"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "infrared colorization using deep convolutional neural networks"
    - "understanding how image quality affects deep neural networks"
    - "colorful image colorization"
    - "a neural algorithm of artistic style"

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)