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: "on learning 3d face morphable model from in the wild images"
  Abstract: As a classic statistical model of 3D facial shape and albedo, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM is learned from a set of well-controlled 2D face images with associated 3D face scans, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as, the linear bases, the representation power of 3DMM can be limited. To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of unconstrained face images, without collecting 3D face scans. Specifically, given a face image as input, a network encoder estimates the projection, illumination, shape and albedo parameters. Two decoders serve as the nonlinear 3DMM to map from the shape and albedo parameters to the 3D shape and albedo, respectively. With the projection parameter, lighting, 3D shape, and albedo, a novel analytically-differentiable rendering layer is designed to reconstruct the original input face. The entire network is end-to-end trainable with only weak supervision. We demonstrate the superior representation power of our nonlinear 3DMM over its linear counterpart, and its contribution to face alignment and 3D reconstruction.

Connected through hyperedge HE-1 (induced by source paper "retinaface single stage dense face localisation in the wild"):
  with members:
    - "robust and high performance face detector"
    - "wider face and pedestrian challenge 2018 methods and results"
    - "improved selective refinement network for face detection"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "make a face towards arbitrary high fidelity face manipulation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "pose guided human video generation"
    - "look at boundary a boundary aware face alignment algorithm"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "self supervised learning of a facial attribute embedding from video"
    - "blind predicting similar quality map for image quality assessment"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "on the detection of digital face manipulation"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "advfaces adversarial face synthesis"
    - "deep tree learning for zero shot face anti spoofing"
    - "representation learning by rotating your faces"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"

Connected through hyperedge HE-4 (induced by source paper "representation learning by rotating your faces"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "additive margin softmax for face verification"
    - "nonlinear 3d face morphable model"
    - "began boundary equilibrium generative adversarial networks"
    - "pose invariant face alignment with a single cnn"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "towards large pose face frontalization in the wild"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-5 (induced by source paper "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "densereg fully convolutional dense shape regression in the wild"
    - "unsupervised training for 3d morphable model regression"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "nonlinear 3d face morphable model"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deformable shape completion with graph convolutional autoencoders"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-6 (induced by source paper "3d face modeling from diverse raw scan data"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "disentangled representation learning for 3d face shape"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning category specific mesh reconstruction from image collections"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "3d coded 3d correspondences by deep deformation"
    - "dense 3d face correspondence"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-7 (induced by source paper "towards high fidelity nonlinear 3d face morphable model"):
  with members:
    - "representation learning by rotating your faces"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "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"
    - "extreme 3d face reconstruction looking past occlusions"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"

Connected through hyperedge HE-8 (induced by source paper "on learning disentangled representations for gait recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "towards high fidelity nonlinear 3d face morphable model"
    - "gait recognition via disentangled representation learning"
    - "retinaface single stage dense face localisation in the wild"
    - "synthesizing images of humans in unseen poses"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "arcface additive angular margin loss for deep face recognition"

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)