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: "pose guided human video generation"
  Abstract: Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation. In the first stage, a Pose Sequence Generative Adversarial Network (PSGAN) learns in an adversarial manner to yield pose sequences conditioned on the class label. In the second stage, a Semantic Consistent Generative Adversarial Network (SCGAN) generates video frames from the poses while preserving coherent appearances in the input image. By enforcing semantic consistency between the generated and ground-truth poses at a high feature level, our SCGAN is robust to noisy or abnormal poses. Extensive experiments on both human action and human face datasets manifest the superiority of the proposed method over other state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "adversarial video generation on complex datasets"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "dance dance generation motion transfer for internet videos"
    - "generating diverse high fidelity images with vq vae 2"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "scaling autoregressive video models"
    - "cgans with projection discriminator"
    - "video to video synthesis"

Connected through hyperedge HE-2 (induced by source paper "reshapegan object reshaping by providing a single reference image"):
  with members:
    - "representation learning by rotating your faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "the gan landscape losses architectures regularization and normalization"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "generative adversarial networks an overview"
    - "an introduction to image synthesis with generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "skeleton aided articulated motion generation"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"
    - "video generation from text"

Connected through hyperedge HE-4 (induced by source paper "360 degree textures of people in clothing from a single image"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "textured neural avatars"
    - "unsupervised part based disentangling of object shape and appearance"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "synthesizing images of humans in unseen poses"
    - "coordinate based texture inpainting for pose guided image generation"

Connected through hyperedge HE-5 (induced by source paper "make a face towards arbitrary high fidelity face manipulation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "on learning 3d face morphable model from in the wild images"
    - "look at boundary a boundary aware face alignment algorithm"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "self supervised learning of a facial attribute embedding from video"
    - "blind predicting similar quality map for image quality assessment"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "g 3 an this video does not exist disentangling motion and appearance for video generation"):
  with members:
    - "video generation from single semantic label map"
    - "guided image to image translation with bi directional feature transformation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "vid2game controllable characters extracted from real world videos"):
  with members:
    - "dense intrinsic appearance flow for human pose transfer"
    - "bottom up object detection by grouping extreme and center points"
    - "video to video synthesis"
    - "cornernet detecting objects as paired keypoints"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "learning 3d human dynamics from video"
    - "sfv reinforcement learning of physical skills from videos"

Connected through hyperedge HE-8 (induced by source paper "multi frame content integration with a spatio temporal attention mechanism for person video motion transfer"):
  with members:
    - "video to video synthesis"
    - "pose flow efficient online pose tracking"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "dense pose transfer"
    - "densepose dense human pose estimation in the wild"
    - "everybody dance now"

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)