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: "learning to generate chairs with convolutional neural networks"
  Abstract: We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.

Connected through hyperedge HE-1 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "deep video based performance cloning"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-2 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-3 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bayesian recurrent neural networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "neural rendering and reenactment of human actor videos"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"

Connected through hyperedge HE-5 (induced by source paper "multi level semantic feature augmentation for one shot learning"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "continual lifelong learning with neural networks a review"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "bridging theory and algorithm for domain adaptation"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"

Connected through hyperedge HE-7 (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-8 (induced by source paper "generalized feedback loop for joint hand object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "dynamics based 3d skeletal hand tracking"

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)