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: "fast and accurate hand shape classification"
  Abstract: The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel, real-time framework for fast hand shape classification. We show how the number of gallery images influences the classification accuracy and execution time of the algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Different methods can be used at each step of the proposed parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "hgr net a fusion network for hand gesture segmentation and recognition"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rethinking atrous convolution for semantic image segmentation"
    - "hand segmentation for hand object interaction from depth map"
    - "densely connected convolutional networks"
    - "convolutional two stream network fusion for video action recognition"
    - "pyramid scene parsing network"
    - "semantic image segmentation via deep parsing network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "segmentation of both diseased and healthy skin from clinical photographs in a primary care setting"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "mask r cnn"
    - "recod titans at isic challenge 2017"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "improving landmark localization with semi supervised learning"):
  with members:
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "improved techniques for training gans"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "theano a python framework for fast computation of mathematical expressions"
    - "recombinator networks learning coarse to fine feature aggregation"

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)