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: "facevr real time facial reenactment and eye gaze control in virtual reality"
  Abstract: We propose FaceVR, a novel image-based method that enables video teleconferencing in VR based on self-reenactment. State-of-the-art face tracking methods in the VR context are focused on the animation of rigged 3d avatars. While they achieve good tracking performance the results look cartoonish and not real. In contrast to these model-based approaches, FaceVR enables VR teleconferencing using an image-based technique that results in nearly photo-realistic outputs. The key component of FaceVR is a robust algorithm to perform real-time facial motion capture of an actor who is wearing a head-mounted display (HMD), as well as a new data-driven approach for eye tracking from monocular videos. Based on reenactment of a prerecorded stereo video of the person without the HMD, FaceVR incorporates photo-realistic re-rendering in real time, thus allowing artificial modifications of face and eye appearances. For instance, we can alter facial expressions or change gaze directions in the prerecorded target video. In a live setup, we apply these newly-introduced algorithmic components.

Connected through hyperedge HE-1 (induced by source paper "on learning 3d face morphable model from in the wild images"):
  with members:
    - "representation learning by rotating your faces"
    - "face alignment in full pose range a 3d total solution"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "unsupervised training for 3d morphable model regression"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "learning dense facial correspondences in unconstrained images"

Connected through hyperedge HE-2 (induced by source paper "unconstrained facial expression transfer using style based generator"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "perceptual losses for real time style transfer and super resolution"
    - "semantic facial expression editing using autoencoded flow"

Connected through hyperedge HE-3 (induced by source paper "frontal screens on head mounted displays to increase awareness of the hmd users state in mixed presence collaboration"):
  with members:
    - "identity preserving face completion for large ocular region occlusion"
    - "eyemotion classifying facial expressions in vr using eye tracking cameras"

Connected through hyperedge HE-4 (induced by source paper "reenactgan learning to reenact faces via boundary transfer"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "style aggregated network for facial landmark detection"
    - "densepose dense human pose estimation in the wild"
    - "quantifying facial age by posterior of age comparisons"
    - "deep structured learning for facial action unit intensity estimation"

Connected through hyperedge HE-5 (induced by source paper "identity preserving face completion for large ocular region occlusion"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "towards large pose face frontalization in the wild"
    - "mask off synthesizing face images in the presence of head mounted displays"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "semantic image inpainting with deep generative models"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-6 (induced by source paper "headon real time reenactment of human portrait videos"):
  with members:
    - "3d face hallucination from a single depth frame"
    - "pose guided person image generation"
    - "deepwarp photorealistic image resynthesis for gaze manipulation"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "automatic face reenactment"

Connected through hyperedge HE-7 (induced by source paper "real time 3d face eye performance capture of a person wearing vr headset"):
  with members:
    - "learning dense facial correspondences in unconstrained images"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "real time facial segmentation and performance capture from rgb input"
    - "deep residual learning for image recognition"
    - "rendering of eyes for eye shape registration and gaze estimation"

Connected through hyperedge HE-8 (induced by source paper "eyemotion classifying facial expressions in vr using eye tracking cameras"):
  with members:
    - "mask off synthesizing face images in the presence of head mounted displays"
    - "eye tracking for everyone"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "the nd iris 0405 iris image dataset"
    - "peak piloted deep network for facial expression recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "facenet a unified embedding for face recognition and clustering"
    - "predicting daily activities from egocentric images using deep learning"

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)