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: "supervised cross modal factor analysis for multiple modal data classification"
  Abstract: In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.

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"
    - "image tag completion by local learning"
    - "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 "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"
    - "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"
    - "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 "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "sparse coding with earth mover s distance for multi instance histogram representation"
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "domain transfer multi instance dictionary learning"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "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"
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"
    - "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 learning of local structured output predictors"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "manifold regularization in structured output space for semi supervised structured output prediction"
    - "representing data by sparse combination of contextual data points for classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-6 (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"
    - "image tag completion by local learning"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"

Connected through hyperedge HE-7 (induced by source paper "domain transfer multi instance dictionary learning"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"

Connected through hyperedge HE-8 (induced by source paper "cross domain adaptation by learning partially shared classifiers and weighting source data points in the shared subspaces"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and 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.LG(Machine Learning)