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: "deep 3d face identification"
  Abstract: We propose a novel 3D face recognition algorithm using a deep convolutional neural network (DCNN) and a 3D augmentation technique. The performance of 2D face recognition algorithms has significantly increased by leveraging the representational power of deep neural networks and the use of large-scale labeled training data. As opposed to 2D face recognition, training discriminative deep features for 3D face recognition is very difficult due to the lack of large-scale 3D face datasets. In this paper, we show that transfer learning from a CNN trained on 2D face images can effectively work for 3D face recognition by fine-tuning the CNN with a relatively small number of 3D facial scans. We also propose a 3D face augmentation technique which synthesizes a number of different facial expressions from a single 3D face scan. Our proposed method shows excellent recognition results on Bosphorus, BU-3DFE, and 3D-TEC datasets, without using hand-crafted features. The 3D identification using our deep features also scales well for large databases.

Connected through hyperedge HE-1 (induced by source paper "data free point cloud network for 3d face recognition"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "arcface additive angular margin loss for deep face recognition"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "dense 3d face correspondence"
    - "learning from millions of 3d scans for large scale 3d face recognition"

Connected through hyperedge HE-2 (induced by source paper "robust rgb d face recognition using attribute aware loss"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "learning from millions of 3d scans for large scale 3d face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "l2 constrained softmax loss for discriminative face verification"
    - "convolutional neural networks for attribute based active authentication on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "face recognition from sequential sparse 3d data via deep registration"):
  with members:
    - "additive margin softmax for face verification"
    - "dense 3d face correspondence"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning from millions of 3d scans for large scale 3d face recognition"
    - "faceposenet making a case for landmark free face alignment"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-4 (induced by source paper "learning from millions of 3d scans for large scale 3d face recognition"):
  with members:
    - "dense 3d face correspondence"
    - "end to end 3d face reconstruction with deep neural networks"
    - "toward open set face recognition"
    - "level playing field for million scale face recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "3d face reconstruction by learning from synthetic data"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)