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: "lookingood enhancing performance capture with real time neural re rendering"
  Abstract: Motivated by augmented and virtual reality applications such as telepresence, there has been a recent focus in real-time performance capture of humans under motion. However, given the real-time constraint, these systems often suffer from artifacts in geometry and texture such as holes and noise in the final rendering, poor lighting, and low-resolution textures. We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time. We call this approach neural (re-)rendering, and our live system "LookinGood". Our deep architecture is trained to produce high resolution and high quality images from a coarse rendering in real-time. First, we propose a self-supervised training method that does not require manual ground-truth annotation. We contribute a specialized reconstruction error that uses semantic information to focus on relevant parts of the subject, e.g. the face. We also introduce a salient reweighing scheme of the loss function that is able to discard outliers. We specifically design the system for virtual and augmented reality headsets where the consistency between the left and right eye plays a crucial role in the final user experience. Finally, we generate temporally stable results by explicitly minimizing the difference between two consecutive frames. We tested the proposed system in two different scenarios: one involving a single RGB-D sensor, and upper body reconstruction of an actor, the second consisting of full body 360 degree capture. Through extensive experimentation, we demonstrate how our system generalizes across unseen sequences and subjects. The supplementary video is available at this http URL

Connected through hyperedge HE-1 (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-2 (induced by source paper "synsin end to end view synthesis from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "differentiable surface splatting for point based geometry processing"
    - "neural point based graphics"
    - "the replica dataset a digital replica of indoor spaces"
    - "deepview view synthesis with learned gradient descent"
    - "linearized multi sampling for differentiable image transformation"
    - "neural volumes learning dynamic renderable volumes from images"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-3 (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"
    - "pose guided human video generation"
    - "synthesizing images of humans in unseen poses"

Connected through hyperedge HE-4 (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"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-5 (induced by source paper "neural re simulation for generating bounces in single images"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "neural rerendering in the wild"
    - "ignor image guided neural object rendering"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to generate long term future via hierarchical prediction"
    - "learning to represent mechanics via long term extrapolation and interpolation"
    - "interpolations of smoke and liquid simulations"

Connected through hyperedge HE-6 (induced by source paper "text based editing of talking head video"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "visemenet audio driven animator centric speech animation"
    - "unsupervised training for 3d morphable model regression"
    - "a hybrid model for identity obfuscation by face replacement"
    - "everybody dance now"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "neural rerendering in the wild"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "multimodal unsupervised image to image translation"
    - "ignor image guided neural object rendering"
    - "drit diverse image to image translation via disentangled representations"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"

Connected through hyperedge HE-8 (induced by source paper "neural volumes learning dynamic renderable volumes from images"):
  with members:
    - "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "video to video synthesis"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deep appearance models for face rendering"
    - "stereo magnification learning view synthesis using multiplane images"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

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)