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: "principal manifolds and nonlinear dimension reduction via local tangent space alignment"
  Abstract: Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry of the manifold using tangent spaces learned by fitting an affine subspace in a neighborhood of each data point. Those tangent spaces are aligned to give the internal global coordinates of the data points with respect to the underlying manifold by way of a partial eigendecomposition of the neighborhood connection matrix. We present a careful error analysis of our algorithm and show that the reconstruction errors are of second-order accuracy. We illustrate our algorithm using curves and surfaces both in #R##N#2D/3D and higher dimensional Euclidean spaces, and 64-by-64 pixel face images with various pose and lighting conditions. We also address several theoretical and algorithmic issues for further research and improvements.

Connected through hyperedge HE-1 (induced by source paper "differential similarity in higher dimensional spaces theory and applications"):
  with members:
    - "clustering coding and the concept of similarity"
    - "what regularized auto encoders learn from the data generating distribution"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "chart auto encoders for manifold structured data"):
  with members:
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "enhanced expressive power and fast training of neural networks by random projections"

Connected through hyperedge HE-3 (induced by source paper "ensemble manifold segmentation for model distillation and semi supervised learning"):
  with members:
    - "improved techniques for training gans"
    - "learning convolutional neural networks for graphs"
    - "semi supervised deep learning by metric embedding"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "cross modal distillation for supervision transfer"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "patch alignment manifold matting"):
  with members:
    - "sparse coding for alpha matting"
    - "what players do with the ball a physically constrained interaction modeling"

Connected through hyperedge HE-5 (induced by source paper "localized lrr on grassmann manifolds an extrinsic view"):
  with members:
    - "low rank representation over the manifold of curves"
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-6 (induced by source paper "nonlinear supervised dimensionality reduction via smooth regular embeddings"):
  with members:
    - "out of sample generalizations for supervised manifold learning for classification"
    - "a study of the classification of low dimensional data with supervised manifold learning"
    - "sparse projections of medical images onto manifolds"
    - "an explicit nonlinear mapping for manifold learning"

Connected through hyperedge HE-7 (induced by source paper "applying ricci flow to high dimensional manifold learning"):
  with members:
    - "locality preserving projection on spd matrix lie group algorithm and analysis"

Connected through hyperedge HE-8 (induced by source paper "partial sum minimization of singular values representation on grassmann manifolds"):
  with members:
    - "convex sparse pca for unsupervised feature learning"
    - "laplacian lrr on product grassmann manifolds for human activity clustering in multi camera video surveillance"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "compressive acquisition of dynamic scenes"
    - "the power of convex relaxation near optimal matrix completion"

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)