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: "photorealistic facial texture inference using deep neural networks"
  Abstract: We present a data-driven inference method that can synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild. After an initial estimation of shape and low-frequency albedo, we compute a high-frequency partial texture map, without the shading component, of the visible face area. To extract the fine appearance details from this incomplete input, we introduce a multi-scale detail analysis technique based on mid-layer feature correlations extracted from a deep convolutional neural network. We demonstrate that fitting a convex combination of feature correlations from a high-resolution face database can yield a semantically plausible facial detail description of the entire face. A complete and photorealistic texture map can then be synthesized by iteratively optimizing for the reconstructed feature correlations. Using these high-resolution textures and a commercial rendering framework, we can produce high-fidelity 3D renderings that are visually comparable to those obtained with state-of-the-art multi-view face capture systems. We demonstrate successful face reconstructions from a wide range of low resolution input images, including those of historical figures. In addition to extensive evaluations, we validate the realism of our results using a crowdsourced user study.

Connected through hyperedge HE-1 (induced by source paper "learned multi view texture super resolution"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "deep learning for single image super resolution a brief review"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep network interpolation for continuous imagery effect transition"

Connected through hyperedge HE-2 (induced by source paper "high accuracy face geometry capture using a smartphone video"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "detailed human avatars from monocular video"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "sparse photometric 3d face reconstruction guided by morphable models"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "robust registration and geometry estimation from unstructured facial scans"

Connected through hyperedge HE-3 (induced by source paper "texture fields learning texture representations in function space"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "learning category specific mesh reconstruction from image collections"
    - "neural processes"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "a neural virtual anchor synthesizer based on seq2seq and gan models"):
  with members:
    - "video synthesis of human upper body with realistic face"
    - "recycle gan unsupervised video retargeting"
    - "deep contextualized word representations"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "everybody dance now"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"):
  with members:
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "arcface additive angular margin loss for deep face recognition"
    - "unsupervised training for 3d morphable model regression"
    - "3d face reconstruction with geometry details from a single image"
    - "deep appearance models for face rendering"
    - "high quality facial surface and texture synthesis via generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "synthesizing facial photometries and corresponding geometries using generative adversarial networks"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "densereg fully convolutional dense shape regression in the wild"
    - "efficient inter geodesic distance computation and fast classical scaling"
    - "high quality facial surface and texture synthesis via generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "mvf net multi view 3d face morphable model regression"):
  with members:
    - "person re identification with deep similarity guided graph neural network"
    - "unsupervised training for 3d morphable model regression"
    - "deep group shuffling random walk for person re identification"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "3d facial expression reconstruction using cascaded regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-8 (induced by source paper "recovering facial reflectance and geometry from multi view images"):
  with members:
    - "real time 3d face eye performance capture of a person wearing vr headset"
    - "learning dense facial correspondences in unconstrained images"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

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)