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: "regularized maximum correntropy machine"
  Abstract: In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two chal- lenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

Connected through hyperedge HE-1 (induced by source paper "supervised learning of sparse context reconstruction coefficients for data representation and classification"):
  with members:
    - "maximum mutual information regularized classification"
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-2 (induced by source paper "preprint bigdata oriented multimedia mobile health applications"):
  with members:
    - "serious game based dysphonic rehabilitation tool"
    - "a game based assistive tool for rehabilitation of dysphonic patients"
    - "preprint clinical feedback and technology selection of game based dysphonic rehabilitation tool"
    - "touch less interactive augmented reality game on vision based wearable device"

Connected through hyperedge HE-3 (induced by source paper "supervised multiview learning based on simultaneous learning of multiview intact and single view classifier"):
  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"

Connected through hyperedge HE-4 (induced by source paper "multiple kernel multivariate performance learning using cutting plane algorithm"):
  with members:
    - "image tag completion by local learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "preprint virtual reality based gis analysis platform"):
  with members:
    - "big city 3d visual analysis"

Connected through hyperedge HE-6 (induced by source paper "preprint webvrgis based traffic analysis and visualization system"):
  with members:
    - "maximum mutual information regularized 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)