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: "object discovery with a copy pasting gan"
  Abstract: We tackle the problem of object discovery, where objects are segmented for a given input image, and the system is trained without using any direct supervision whatsoever. A novel copy-pasting GAN framework is proposed, where the generator learns to discover an object in one image by compositing it into another image such that the discriminator cannot tell that the resulting image is fake. After carefully addressing subtle issues, such as preventing the generator from `cheating', this game results in the generator learning to select objects, as copy-pasting objects is most likely to fool the discriminator. The system is shown to work well on four very different datasets, including large object appearance variations in challenging cluttered backgrounds.

Connected through hyperedge HE-1 (induced by source paper "multiple identity image attacks against face based identity verification"):
  with members:
    - "reflection analysis for face morphing attack detection"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "latent space autoregression for novelty detection"
    - "a style based generator architecture for generative adversarial networks"
    - "additive margin softmax for face verification"
    - "identifying bias in ai using simulation"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "genesis generative scene inference and sampling with object centric latent representations"):
  with members:
    - "emergence of object segmentation in perturbed generative models"
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised object segmentation by redrawing"

Connected through hyperedge HE-3 (induced by source paper "investigating object compositionality in generative adversarial networks"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "emergence of object segmentation in perturbed generative models"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "unsupervised object segmentation by redrawing"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the gan landscape losses architectures regularization and normalization"
    - "relational deep reinforcement learning"

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)