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: "improving shadow suppression for illumination robust face recognition"
  Abstract: 2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. An illumination robust preprocessing method thus remains a significant challenge in reliable face analysis. In this paper we propose a novel approach for improving lighting normalization through building the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborates the formation of face color appearance. Specifically, the proposed illumination processing pipeline enables the generation of Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach to deal with lighting variations, including both soft and hard shadows, in face recognition.

Connected through hyperedge HE-1 (induced by source paper "region homogeneity in the logarithmic image processing framework application to region growing algorithms"):
  with members:
    - "homogeneity of a region in the logarithmic image processing framework application to region growing algorithms"
    - "double sided probing by map of asplund s distances using logarithmic image processing in the framework of mathematical morphology"
    - "superimposition of eye fundus images for longitudinal analysis from large public health databases"
    - "spatio colour asplund s metric and logarithmic image processing for colour images lipc"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-2 (induced by source paper "a double deep spatio angular learning framework for light field based face recognition"):
  with members:
    - "face recognition a novel multi level taxonomy based survey"
    - "deep face recognition a survey"
    - "personalized age progression with bi level aging dictionary learning"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "regularizing and optimizing lstm language models"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a comprehensive analysis of deep learning based representation for 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"
    - "automatic face image quality prediction"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"

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)