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: "semantic adversarial attacks parametric transformations that fool deep classifiers"
  Abstract: Deep neural networks have been shown to exhibit an intriguing vulnerability to adversarial input images corrupted with imperceptible perturbations. However, the majority of adversarial attacks assume global, fine-grained control over the image pixel space. In this paper, we consider a different setting: what happens if the adversary could only alter specific attributes of the input image? These would generate inputs that might be perceptibly different, but still natural-looking and enough to fool a classifier. We propose a novel approach to generate such `semantic' adversarial examples by optimizing a particular adversarial loss over the range-space of a parametric conditional generative model. We demonstrate implementations of our attacks on binary classifiers trained on face images, and show that such natural-looking semantic adversarial examples exist. We evaluate the effectiveness of our attack on synthetic and real data, and present detailed comparisons with existing attack methods. We supplement our empirical results with theoretical bounds that demonstrate the existence of such parametric adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "colorfool semantic adversarial colorization"
    - "functional adversarial attacks"
    - "a general framework for adversarial examples with objectives"
    - "panoptic feature pyramid networks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "type i attack for generative models"):
  with members:
    - "colorfool semantic adversarial colorization"
    - "adversarial attacks on variational autoencoders"
    - "semantic adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "adversarial attack type i cheat classifiers by significant changes"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-3 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "generating semantic adversarial examples with differentiable rendering"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "counterexample guided data augmentation"
    - "3d aware scene manipulation via inverse graphics"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-5 (induced by source paper "spatiotemporally constrained action space attacks on deep reinforcement learning agents"):
  with members:
    - "action robust reinforcement learning and applications in continuous control"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "differentiable mpc for end to end planning and control"
    - "sequential attacks on agents for long term adversarial goals"
    - "online robust policy learning in the presence of unknown adversaries"
    - "gradient band based adversarial training for generalized attack immunity of a3c path finding"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-6 (induced by source paper "towards verifying robustness of neural networks against semantic perturbations"):
  with members:
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "towards analyzing semantic robustness of deep neural networks"
    - "scaling provable adversarial defenses"
    - "semantic adversarial examples"
    - "a dual approach to scalable verification of deep networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "efficient formal safety analysis of 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)