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: "video generation from single semantic label map"
  Abstract: This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process. Different from typical end-to-end approaches, which model both scene content and dynamics in a single step, we propose to decompose this difficult task into two sub-problems. As current image generation methods do better than video generation in terms of detail, we synthesize high quality content by only generating the first frame. Then we animate the scene based on its semantic meaning to obtain the temporally coherent video, giving us excellent results overall. We employ a cVAE for predicting optical flow as a beneficial intermediate step to generate a video sequence conditioned on the initial single frame. A semantic label map is integrated into the flow prediction module to achieve major improvements in the image-to-video generation process. Extensive experiments on the Cityscapes dataset show that our method outperforms all competing methods.

Connected through hyperedge HE-1 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving"):
  with members:
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "infer intermediate representations for future prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "starnet pedestrian trajectory prediction using deep neural network in star topology"
    - "peeking into the future predicting future person activities and locations in videos"
    - "video to video synthesis"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"

Connected through hyperedge HE-2 (induced by source paper "few shot video to video synthesis"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "dance dance generation motion transfer for internet videos"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "vid2game controllable characters extracted from real world videos"
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-3 (induced by source paper "g 3 an this video does not exist disentangling motion and appearance for video generation"):
  with members:
    - "guided image to image translation with bi directional feature transformation"
    - "pose guided human video generation"
    - "learning to forecast and refine residual motion for image to video generation"
    - "smit stochastic multi label image to image translation"
    - "video prediction with appearance and motion conditions"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "flow grounded spatial temporal video prediction from still images"
    - "spectral normalization for generative adversarial 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)