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: "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"
  Abstract: Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover's distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stochastic learning framework, we have one triplet of bags, including one basic bag, one positive bag, and one negative bag. These bags are mapped to histograms using a multi-instance dictionary. We argue that the EMD between the basic histogram and the positive histogram should be smaller than that between the basic histogram and the negative histogram. Base on this condition, we design a hinge loss. By minimizing this hinge loss and some regularization terms of the dictionary, we update the dictionary instances. The experiments over multi-instance retrieval applications shows its effectiveness when compared to other dictionary learning methods over the problems of medical image retrieval and natural language relation classification.

Connected through hyperedge HE-1 (induced by source paper "a novel image tag completion method based on convolutional neural network"):
  with members:
    - "learning convolutional neural network to maximize pos top performance measure"
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"
    - "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:
    - "learning convolutional neural network to maximize pos top performance measure"
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"
    - "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"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"
    - "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"

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)