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: "docface matching id document photos to selfies"
  Abstract: Numerous activities in our daily life, including transactions, access to services and transportation, require us to verify who we are by showing our ID documents containing face images, e.g. passports and driver licenses. An automatic system for matching ID document photos to live face images in real time with high accuracy would speedup the verification process and remove the burden on human operators. In this paper, by employing the transfer learning technique, we propose a new method, DocFace, to train a domain-specific network for ID document photo matching without a large dataset. Compared with the baseline of applying existing methods for general face recognition to this problem, our method achieves considerable improvement. A cross validation on an ID-Selfie dataset shows that DocFace improves the TAR from 61.14% to 92.77% at FAR=0.1%. Experimental results also indicate that given more training data, a viable system for automatic ID document photo matching can be developed and deployed.

Connected through hyperedge HE-1 (induced by source paper "reducing geographic performance differential for face recognition"):
  with members:
    - "characterizing the variability in face recognition accuracy relative to race"
    - "accuracy comparison across face recognition algorithms where are we on measuring race bias"
    - "identity document to selfie face matching across adolescence"
    - "deep learning is robust to massive label noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "docface id document to selfie matching"
    - "an empirical study of example forgetting during deep neural network learning"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-2 (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 id document to selfie matching"
    - "cross domain deep face matching for real banking security systems"
    - "large scale bisample learning on id vs spot face recognition"
    - "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-3 (induced by source paper "face detection in camera captured images of identity documents under challenging conditions"):
  with members:
    - "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "ssh single stage headless face detector"
    - "grid loss detecting occluded faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "wider face a face detection benchmark"
    - "from facial parts responses to face detection a deep learning approach"

Connected through hyperedge HE-4 (induced by source paper "docface id document to selfie matching"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "large scale bisample learning on id vs spot face recognition"
    - "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"

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)