human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "von mises fisher mixture model based deep learning application to face verification"):
  Abstract: A number of pattern recognition tasks, \textit{e.g.}, face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distance can be simply computed by their angle. In this paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical foundation for an effective deep-learning of such directional features and derive a novel vMF Mixture Loss and its corresponding vMF deep features. The proposed vMF feature learning achieves the characteristics of discriminative learning, \textit{i.e.}, compacting the instances of the same class while increasing the distance of instances from different classes. Moreover, it subsumes a number of popular loss functions as well as an effective method in deep learning, namely normalization. We conduct extensive experiments on face verification using 4 different challenging face datasets, \textit{i.e.}, LFW, YouTube faces, CACD and IJB-A. Results show the effectiveness and excellent generalization ability of the proposed approach as it achieves state-of-the-art results on the LFW, YouTube faces and CACD datasets and competitive results on the IJB-A dataset.

Includes members:
  Member "sphereface deep hypersphere embedding for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "face aging with conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "l2 constrained softmax loss for discriminative face verification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalizing face representation with unlabeled data")
  Member "cross age lfw a database for studying cross age face recognition in unconstrained environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "xcos an explainable cosine metric for face verification task")
  Member "automatic face image quality prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on measuring the iconicity of a face")
  Member "deepvisage making face recognition simple yet with powerful generalization skills":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalizing face representation with unlabeled data")
  Member "cosine normalization using cosine similarity instead of dot product in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "extended batch normalization")

Question: Please tell me which class the source paper 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)