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: "multiview hessian regularization for image annotation"
  Abstract: The rapid development of computer hardware and Internet technology makes large scale data dependent models computationally tractable, and opens a bright avenue for annotating images through innovative machine learning algorithms. Semisupervised learning (SSL) therefore received intensive attention in recent years and was successfully deployed in image annotation. One representative work in SSL is Laplacian regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it is observed that LR biases the classification function toward a constant function that possibly results in poor generalization. In addition, LR is developed to handle uniformly distributed data (or single-view data), although instances or objects, such as images and videos, are usually represented by multiview features, such as color, shape, and texture. In this paper, we present multiview Hessian regularization (mHR) to address the above two problems in LR-based image annotation. In particular, mHR optimally combines multiple HR, each of which is obtained from a particular view of instances, and steers the classification function that varies linearly along the data manifold. We apply mHR to kernel least squares and support vector machines as two examples for image annotation. Extensive experiments on the PASCAL VOC'07 dataset validate the effectiveness of mHR by comparing it with baseline algorithms, including LR and HR.

Connected through hyperedge HE-1 (induced by source paper "multi view intact space learning"):
  with members:
    - "a survey on multi view learning"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "active learning with multiple views"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-2 (induced by source paper "hypergraph p laplacian regularization for remotely sensed image recognition"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "ensemble p laplacian regularization for remote sensing image recognition"):
  with members:
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "multiview hessian regularized logistic regression for action recognition"):
  with members:
    - "multiview hessian discriminative sparse coding for image annotation"
    - "a survey on multi view learning"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-5 (induced by source paper "multi view face analysis based on gabor features"):
  with members:
    - "multiview hessian discriminative sparse coding for image annotation"

Connected through hyperedge HE-6 (induced by source paper "large scale paralleled sparse principal component analysis"):
  with members:
    - "multiview hessian discriminative sparse coding for image annotation"
    - "optimal solutions for sparse principal component analysis"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-7 (induced by source paper "multiview hessian discriminative sparse coding for image annotation"):
  with members:
    - "a survey on multi view learning"

Connected through hyperedge HE-8 (induced by source paper "manifold regularized kernel logistic regression for web image annotation"):
  with members:
    - "multiview hessian discriminative sparse coding for image annotation"

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)