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: "towards an understanding of neural networks in natural image spaces"
  Abstract: Two major uncertainties, dataset bias and perturbation, prevail in state-of-the-art AI algorithms with deep neural networks. In this paper, we present an intuitive explanation for these issues as well as an interpretation of the performance of deep networks in a natural-image space. The explanation consists of two parts: the philosophy of neural networks and a hypothetic model of natural-image spaces. Following the explanation, we slightly improve the accuracy of a CIFAR-10 classifier by introducing an additional "random-noise" category during training. We hope this paper will stimulate discussion in the community regarding the topological and geometric properties of natural-image spaces to which deep networks are applied.

Connected through hyperedge HE-1 (induced by source paper "are you tampering with my data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "peernets exploiting peer wisdom against adversarial attacks"

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)