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: "large scale bisample learning on id vs spot face recognition"
  Abstract: In many face recognition applications, there is large amount of face data with two images for each person. One is an ID photo for face enrollment, and the other is a probe photo captured on spot. Most existing methods are designed for training data with limited breadth (relatively small class number) and sufficient depth (many samples for each class). They would meet great challenges when applied on this ID vs. Spot (IvS) data, including the under-represented intra-class variations and the excessive demand on computing devices. In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition. To tackle the bisample problem that there are only two samples for each class, a classification-verification-classification (CVC) training strategy is proposed to progressively enhance the IvS performance. Besides, a dominant prototype softmax (DP-softmax) is incorporated to make the deep learning applicable on large-scale classes. We conduct LBL on a IvS face dataset with more than two million identities. Experimental results show the proposed method achieves superior performance than previous ones, validating the effectiveness of LBL on IvS face recognition.

Connected through hyperedge HE-1 (induced by source paper "identity document to selfie face matching across adolescence"):
  with members:
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "docface matching id document photos to selfies"
    - "docface id document to selfie matching"
    - "cross domain deep face matching for real banking security systems"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "longitudinal study of child face recognition"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"

Connected through hyperedge HE-2 (induced by source paper "docface id document to selfie matching"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "docface matching id document photos to selfies"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-3 (induced by source paper "docface matching id document photos to selfies"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"

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)