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: "adversarial attacks for optical flow based action recognition classifiers"
  Abstract: The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video domains. However, recent work has shown that these largely uninterpretable models exhibit glaring security vulnerabilities in the presence of an adversary. In this work, we develop a powerful untargeted adversarial attack for action recognition systems in both white-box and black-box settings. Action recognition models differ from image-classification models in that their inputs contain a temporal dimension, which we explicitly target in the attack. Drawing inspiration from image classifier attacks, we create new attacks which achieve state-of-the-art success rates on a two-stream classifier trained on the UCF-101 dataset. We find that our attacks can significantly degrade a model's performance with sparsely and imperceptibly perturbed examples. We also demonstrate the transferability of our attacks to black-box action recognition systems.

Connected through hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior"):
  with members:
    - "sparse black box video attack with reinforcement learning"
    - "heuristic black box adversarial attacks on video recognition models"
    - "deep video inpainting"
    - "appending adversarial frames for universal video attack"
    - "sequence level semantics aggregation for video object detection"
    - "black box adversarial attacks on video recognition models"
    - "sparse adversarial perturbations for videos"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "smart skeletal motion action recognition attack"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "meshadv adversarial meshes for visual recognition"
    - "adversarial attacks on time series"
    - "adversarial attack on skeleton based human action recognition"
    - "enhancing adversarial example transferability with an intermediate level attack"
    - "spatio temporal manifold learning for human motions via long horizon modeling"
    - "spatially transformed adversarial examples"
    - "learning discriminative video representations using adversarial perturbations"

Connected through hyperedge HE-3 (induced by source paper "robustness guarantees for deep neural networks on videos"):
  with members:
    - "verification of recurrent neural networks through rule extraction"
    - "sparse adversarial perturbations for videos"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "universal adversarial perturbations"
    - "safety verification of deep neural networks"

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)