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: "deepwarp photorealistic image resynthesis for gaze manipulation"
  Abstract: In this work, we consider the task of generating highly-realistic images of a given face with a redirected gaze. We treat this problem as a specific instance of conditional image generation and suggest a new deep architecture that can handle this task very well as revealed by numerical comparison with prior art and a user study. Our deep architecture performs coarse-to-fine warping with an additional intensity correction of individual pixels. All these operations are performed in a feed-forward manner, and the parameters associated with different operations are learned jointly in the end-to-end fashion. After learning, the resulting neural network can synthesize images with manipulated gaze, while the redirection angle can be selected arbitrarily from a certain range and provided as an input to the network.

Connected through hyperedge HE-1 (induced by source paper "textured neural avatars"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "deep video portraits"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "densereg fully convolutional dense shape regression in the wild"
    - "deep video based performance cloning"
    - "lookingood enhancing performance capture with real time neural re rendering"

Connected through hyperedge HE-2 (induced by source paper "neural rendering and reenactment of human actor videos"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "view extrapolation of human body from a single image"
    - "a variational u net for conditional appearance and shape generation"
    - "pose guided person image generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "carla an open urban driving simulator"
    - "a generative model of people in clothing"
    - "deformable gans for pose based human image generation"

Connected through hyperedge HE-3 (induced by source paper "neural rendering and reenactment of human actor videos"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "view extrapolation of human body from a single image"
    - "progressive growing of gans for improved quality stability and variation"
    - "carla an open urban driving simulator"
    - "a generative model of people in clothing"
    - "deformable gans for pose based human image generation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "video enhancement with task oriented flow"):
  with members:
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "multi frame quality enhancement for compressed video"
    - "context aware synthesis for video frame interpolation"
    - "flow guided feature aggregation for video object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "accurate optical flow via direct cost volume processing"

Connected through hyperedge HE-5 (induced by source paper "unsupervised bi directional flow based video generation from one snapshot"):
  with members:
    - "video enhancement with task oriented flow"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "flow grounded spatial temporal video prediction from still images"
    - "dual motion gan for future flow embedded video prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "mocogan decomposing motion and content for video generation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "unsupervised learning of long term motion dynamics for videos"

Connected through hyperedge HE-6 (induced by source paper "adaptis adaptive instance selection network"):
  with members:
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "bag of freebies for training object detection neural networks"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"
    - "learning to fuse things and stuff"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "few shot adversarial learning of realistic neural talking head models"):
  with members:
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "deep video portraits"
    - "neural voice cloning with a few samples"
    - "arcface additive angular margin loss for deep face recognition"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "icface interpretable and controllable face reenactment using gans"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "facelet bank for fast portrait manipulation"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "cr gan learning complete representations for multi view generation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "ganimation anatomically aware facial animation from a single image"
    - "the contextual loss for image transformation with non aligned data"
    - "which training methods for gans do actually converge"

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)