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: "3d facial expression reconstruction using cascaded regression"
  Abstract: This paper proposes a novel model fitting algorithm for 3D facial expression reconstruction from a single image. Face expression reconstruction from a single image is a challenging task in computer vision. Most state-of-the-art methods fit the input image to a 3D Morphable Model (3DMM). These methods need to solve a stochastic problem and cannot deal with expression and pose variations. To solve this problem, we adopt a 3D face expression model and use a combined feature which is robust to scale, rotation and different lighting conditions. The proposed method applies a cascaded regression framework to estimate parameters for the 3DMM. 2D landmarks are detected and used to initialize the 3D shape and mapping matrices. In each iteration, residues between the current 3DMM parameters and the ground truth are estimated and then used to update the 3D shapes. The mapping matrices are also calculated based on the updated shapes and 2D landmarks. HOG features of the local patches and displacements between 3D landmark projections and 2D landmarks are exploited. Compared with existing methods, the proposed method is robust to expression and pose changes and can reconstruct higher fidelity 3D face shape.

Connected through hyperedge HE-1 (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"
    - "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"
    - "unrestricted facial geometry reconstruction using image to image translation"

Connected through hyperedge HE-2 (induced by source paper "person re identification with deep similarity guided graph neural network"):
  with members:
    - "end to end deep kronecker product matching for person re identification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "harmonious attention network for person re identification"
    - "multi channel pyramid person matching network for person re identification"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "deep group shuffling random walk for person re identification"
    - "vip cnn visual phrase guided convolutional neural network"
    - "random erasing data augmentation"

Connected through hyperedge HE-3 (induced by source paper "end to end deep kronecker product matching for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "deeply learned part aligned representations for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "hydraplus net attentive deep features for pedestrian analysis"

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)