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: "evaluating robustness of deep image super resolution against adversarial attacks"
  Abstract: Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision applications. This paper investigates the robustness of deep learning-based super-resolution methods against adversarial attacks, which can significantly deteriorate the super-resolved images without noticeable distortion in the attacked low-resolution images. It is demonstrated that state-of-the-art deep super-resolution methods are highly vulnerable to adversarial attacks. Different levels of robustness of different methods are analyzed theoretically and experimentally. We also present analysis on transferability of attacks, and feasibility of targeted attacks and universal attacks.

Connected through hyperedge HE-1 (induced by source paper "improving robustness of deep learning based image reconstruction"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "gan based projector for faster recovery with convergence guarantees in linear inverse problems"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "scaling provable adversarial defenses"
    - "adversarially robust generalization requires more data"
    - "generating adversarial examples with adversarial networks"
    - "image reconstruction by domain transform manifold learning"

Connected through hyperedge HE-2 (induced by source paper "learning to have an ear for face super resolution"):
  with members:
    - "seeing what a gan cannot generate"
    - "what makes training multi modal networks hard"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "two stream action recognition oriented video super resolution"
    - "image super resolution by neural texture transfer"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "zoom to learn learn to zoom"
    - "speech2face learning the face behind a voice"

Connected through hyperedge HE-3 (induced by source paper "appending adversarial frames for universal video attack"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "black box adversarial attacks on video recognition models"
    - "sparse adversarial perturbations for videos"
    - "adversarial perturbations against real time video classification systems"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "adversarial examples for semantic segmentation and object detection"

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)