human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "nonlinear supervised dimensionality reduction via smooth regular embeddings"):
  Abstract: The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years. Meanwhile, existing methods primarily focus on the embedding of the training data, and the generalization of the embedding to initially unseen test data is rather ignored. In this work, we build on recent theoretical results on the generalization performance of supervised manifold learning algorithms. Motivated by these performance bounds, we propose a supervised manifold learning method that computes a nonlinear embedding while constructing a smooth and regular interpolation function that extends the embedding to the whole data space in order to achieve satisfactory generalization. The embedding and the interpolator are jointly learnt such that the Lipschitz regularity of the interpolator is imposed while ensuring the separation between different classes. Experimental results on several image data sets show that the proposed method yields quite satisfactory performance in comparison with other supervised dimensionality reduction algorithms and traditional classifiers.

Includes members:
  Member "out of sample generalizations for supervised manifold learning for classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a physics constrained data driven approach based on locally convex reconstruction for noisy database")
  Member "a study of the classification of low dimensional data with supervised manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised co learning on mathcal g manifolds across irreducible representations")
  Member "sparse projections of medical images onto manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "out of sample generalizations for supervised manifold learning for classification")
  Member "an explicit nonlinear mapping for manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "out of sample generalizations for supervised manifold learning for classification")
  Member "principal manifolds and nonlinear dimension reduction via local tangent space alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differential similarity in higher dimensional spaces theory and applications")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)