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: "optimizing top precision performance measure of content based image retrieval by learning similarity function"
  Abstract: In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

Connected through hyperedge HE-1 (induced by source paper "a novel image tag completion method based on convolutional neural network"):
  with members:
    - "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"
    - "learning convolutional neural network to maximize pos top performance measure"
    - "a novel transfer learning method based on common space mapping and weighted domain matching"

Connected through hyperedge HE-2 (induced by source paper "cross model convolutional neural network for multiple modality data representation"):
  with members:
    - "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"
    - "learning convolutional neural network to maximize pos top performance measure"
    - "a novel transfer learning method based on common space mapping and weighted domain matching"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "caffe convolutional architecture for fast feature embedding"
    - "a convolutional neural network for modelling sentences"
    - "multimodal similarity preserving hashing"

Connected through hyperedge HE-3 (induced by source paper "learning convolutional neural network to maximize pos top performance measure"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "a novel transfer learning method based on common space mapping and weighted domain matching"
    - "top rank optimization in linear time"
    - "an evasion and counter evasion study in malicious websites detection"

Connected through hyperedge HE-4 (induced by source paper "a novel transfer learning method based on common space mapping and weighted domain matching"):
  with members:
    - "supporting regularized logistic regression privately and efficiently"
    - "semi supervised structured output prediction by local linear regression and sub gradient descent"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "an evasion and counter evasion study in malicious websites detection"

Connected through hyperedge HE-5 (induced by source paper "semi supervised structured output prediction by local linear regression and sub gradient descent"):
  with members:
    - "manifold regularization in structured output space for semi supervised structured output prediction"
    - "image tag completion by local learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"

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)