human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "disrupting deepfakes adversarial attacks against conditional image translation networks and facial manipulation systems"):
  Abstract: Face modification systems using deep learning have become increasingly powerful and accessible. Given images of a person's face, such systems can generate new images of that same person under different expressions and poses. Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined DeepFakes. In order to prevent a malicious user from generating modified images of a person without their consent we tackle the new problem of generating adversarial attacks against image translation systems, which disrupt the resulting output image. We call this problem disrupting deepfakes. We adapt traditional adversarial attacks to our scenario. Most image translation architectures are generative models conditioned on an attribute (e.g. put a smile on this person's face). We present class transferable adversarial attacks that generalize to different classes, which means that the attacker does not need to have knowledge about the conditioning vector. In gray-box scenarios, blurring can mount a successful defense against disruption. We present a spread-spectrum adversarial attack, which evades blurring defenses.

Includes members:
  Member "deceiving image to image translation networks for autonomous driving with adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "fakespotter a simple baseline for spotting ai synthesized fake faces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "few shot adversarial learning of realistic neural talking head models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "opengan open set generative adversarial networks")
  Member "stargan v2 diverse image synthesis for multiple domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "cnn generated images are surprisingly easy to spot for now":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans")
  Member "ganimation anatomically aware facial animation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

Question: Please tell me which class the source paper 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)