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: "automatic face image quality prediction"
  Abstract: Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcher-independent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.

Connected through hyperedge HE-1 (induced by source paper "on measuring the iconicity of a face"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "self normalizing neural networks"
    - "l2 constrained softmax loss for discriminative face verification"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "triplet probabilistic embedding for face verification and clustering"
    - "an all in one convolutional neural network for face analysis"

Connected through hyperedge HE-2 (induced by source paper "deep face quality assessment"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "patch based probabilistic image quality assessment for face selection and improved video based face recognition"

Connected through hyperedge HE-3 (induced by source paper "von mises fisher mixture model based deep learning application to face verification"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "face aging with conditional generative adversarial networks"
    - "l2 constrained softmax loss for discriminative face verification"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "improving shadow suppression for illumination robust 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)