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: "image tag completion by local learning"
  Abstract: The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parame- ters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages.

Connected through hyperedge HE-1 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "representing data by sparse combination of contextual data points for classification"
    - "remote health coaching system and human motion data analysis for physical therapy with microsoft kinect"
    - "evaluation of pose tracking accuracy in the first and second generations of microsoft kinect"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"

Connected through hyperedge HE-2 (induced by source paper "semi supervised structured output prediction by local linear regression and sub gradient descent"):
  with members:
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"
    - "manifold regularization in structured output space for semi supervised structured output prediction"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"

Connected through hyperedge HE-3 (induced by source paper "sparse learning of maximum likelihood model for optimization of complex loss function"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "representing data by sparse combination of contextual data points for classification"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "a feature selection method for multivariate performance measures"
    - "efficient optimization of performance measures by classifier adaptation"

Connected through hyperedge HE-4 (induced by source paper "multiple kernel multivariate performance learning using cutting plane algorithm"):
  with members:
    - "regularized maximum correntropy machine"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "a feature selection method for multivariate performance measures"
    - "efficient optimization of performance measures by classifier adaptation"
    - "multiple graph regularized protein domain ranking"

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)