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: "unsupervised video to video translation"
  Abstract: Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time. In this paper, we formulate a new task of unsupervised video-to-video translation, which poses its own unique challenges. Translating video implies learning not only the appearance of objects and scenes but also realistic motion and transitions between consecutive frames.We investigate the performance of per-frame video-to-video translation using existing image-to-image translation networks, and propose a spatio-temporal 3D translator as an alternative solution to this problem. We evaluate our 3D method on multiple synthetic datasets, such as moving colorized digits, as well as the realistic segmentation-to-video GTA dataset and a new CT-to-MRI volumetric images translation dataset. Our results show that frame-wise translation produces realistic results on a single frame level but underperforms significantly on the scale of the whole video compared to our three-dimensional translation approach, which is better able to learn the complex structure of video and motion and continuity of object appearance.

Connected through hyperedge HE-1 (induced by source paper "joint wasserstein distribution matching"):
  with members:
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"
    - "improving shape deformation in unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "unsupervised attention guided image to image translation"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "deep view morphing"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "adversarial self defense for cycle consistent gans"):
  with members:
    - "a two stream variational adversarial network for video generation"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "deep defense training dnns with improved adversarial robustness"
    - "cycada cycle consistent adversarial domain adaptation"
    - "progressive growing of gans for improved quality stability and variation"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-3 (induced by source paper "recycle gan unsupervised video retargeting"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "probabilistic video generation using holistic attribute control"
    - "learning to generate long term future via hierarchical prediction"
    - "cycada cycle consistent adversarial domain adaptation"
    - "pixelnn example based image synthesis"
    - "unsupervised learning of depth and ego motion from video"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "actionvlad learning spatio temporal aggregation for action classification"

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)