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: "multiple kernel multivariate performance learning using cutting plane algorithm"
  Abstract: In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the kernel weight are unified in a single objective function considering to minimize the upper boundary of the given multivariate performance measure. The objective function is optimized with regard to classifier parameter and kernel weight alternately in an iterative algorithm by using cutting plane algorithm. The developed algorithm is evaluated on two different pattern classification methods with regard to various multivariate performance measure optimization problems. The experiment results show the proposed algorithm outperforms the competing 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"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"

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"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "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"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "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"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "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"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "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"
    - "supervised cross modal factor analysis for multiple modal data classification"

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"
    - "supervised cross modal factor analysis for multiple modal data classification"

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"
    - "supervised cross modal factor analysis for multiple modal data classification"

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)