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: "attentive semantic video generation using captions"
  Abstract: This paper proposes a network architecture to perform variable length semantic video generation using captions. We adopt a new perspective towards video generation where we allow the captions to be combined with the long-term and short-term dependencies between video frames and thus generate a video in an incremental manner. Our experiments demonstrate our network architecture's ability to distinguish between objects, actions and interactions in a video and combine them to generate videos for unseen captions. The network also exhibits the capability to perform spatio-temporal style transfer when asked to generate videos for a sequence of captions. We also show that the network's ability to learn a latent representation allows it generate videos in an unsupervised manner and perform other tasks such as action recognition. (Accepted in International Conference in Computer Vision (ICCV) 2017)

Connected through hyperedge HE-1 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "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"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"
    - "video generation from text"

Connected through hyperedge HE-2 (induced by source paper "point to point video generation"):
  with members:
    - "diversity sensitive conditional generative adversarial networks"
    - "phasenet for video frame interpolation"
    - "conditional video generation using action appearance captions"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "self supervised learning of a facial attribute embedding from video"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"

Connected through hyperedge HE-3 (induced by source paper "hierarchical cross modal talking face generationwith dynamic pixel wise loss"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "generating talking face landmarks from speech"
    - "hierarchical long term video prediction without supervision"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "talking face generation by conditional recurrent adversarial network"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-4 (induced by source paper "deep video generation prediction and completion of human action sequences"):
  with members:
    - "learning to generate long term future via hierarchical prediction"
    - "pose guided person image generation"
    - "skeleton aided articulated motion generation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "improved training of wasserstein gans"
    - "video frame interpolation via adaptive separable convolution"
    - "disentangled person image generation"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-5 (induced by source paper "video prediction with appearance and motion conditions"):
  with members:
    - "dual motion gan for future flow embedded video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "forecasting human dynamics from static images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "unsupervised learning for physical interaction through video prediction"
    - "improved techniques for training gans"

Connected through hyperedge HE-6 (induced by source paper "imagine this scripts to compositions to videos"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "learning to generate long term future via hierarchical prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "aligned image word representations improve inductive transfer across vision language tasks"
    - "photographic image synthesis with cascaded refinement networks"
    - "auto painter cartoon image generation from sketch by using conditional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "conditional video generation using action appearance captions"):
  with members:
    - "pose guided human video generation"
    - "generative adversarial image synthesis with decision tree latent controller"
    - "deep video generation prediction and completion of human action sequences"
    - "probabilistic video generation using holistic attribute control"
    - "to create what you tell generating videos from captions"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "temporal generative adversarial nets with singular value clipping"

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)