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: "every smile is unique landmark guided diverse smile generation"
  Abstract: Each smile is unique: one person surely smiles in different ways (e.g., closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video generation problem, we propose a novel deep learning architecture named Conditional Multi-Mode Network (CMM-Net). To better encode the dynamics of facial expressions, CMM-Net explicitly exploits facial landmarks for generating smile sequences. Specifically, a variational auto-encoder is used to learn a facial landmark embedding. This single embedding is then exploited by a conditional recurrent network which generates a landmark embedding sequence conditioned on a specific expression (e.g., spontaneous smile). Next, the generated landmark embeddings are fed into a multi-mode recurrent landmark generator, producing a set of landmark sequences still associated to the given smile class but clearly distinct from each other. Finally, these landmark sequences are translated into face videos. Our experimental results demonstrate the effectiveness of our CMM-Net in generating realistic videos of multiple smile expressions.

Connected through hyperedge HE-1 (induced by source paper "first order motion model for image animation"):
  with members:
    - "textured neural avatars"
    - "few shot adversarial learning of realistic neural talking head models"
    - "dwnet dense warp based network for pose guided human video generation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesture to gesture translation in the wild via category independent conditional maps"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "learning to forecast and refine residual motion for image to video generation"

Connected through hyperedge HE-2 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "cross view image synthesis using conditional gans"

Connected through hyperedge HE-3 (induced by source paper "dual generator generative adversarial networks for multi domain image to image translation"):
  with members:
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "generative face completion"
    - "combogan unrestrained scalability for image domain translation"

Connected through hyperedge HE-4 (induced by source paper "transgaga geometry aware unsupervised image to image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "unsupervised discovery of object landmarks as structural representations"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-5 (induced by source paper "a novel bilevel paradigm for image to image translation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "few shot segmentation propagation with guided networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "dwnet dense warp based network for pose guided human video generation"):
  with members:
    - "video to video synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "soft gated warping gan for pose guided person image synthesis"
    - "learning to forecast and refine residual motion for image to video generation"
    - "synthesizing images of humans in unseen poses"
    - "a variational u net for conditional appearance and shape generation"
    - "dense pose transfer"
    - "animating arbitrary objects via deep motion transfer"

Connected through hyperedge HE-7 (induced by source paper "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "learning to forecast and refine residual motion for image to video generation"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "controllable image to video translation a case study on facial expression generation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-8 (induced by source paper "diversity in faces"):
  with members:
    - "the devil of face recognition is in the noise"
    - "women also snowboard overcoming bias in captioning models"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation of both diseased and healthy skin from clinical photographs in a primary care setting"
    - "bias mitigation post processing for individual and group fairness"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "progressive growing of gans for improved quality stability and variation"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"

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)