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: "clustering with similarity preserving"
  Abstract: Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time. Specifically, we require the learned graph be close to a kernel matrix, which serves as a measure of similarity in raw data. Moreover, the structure is adaptively tuned so that the number of connected components of the graph is exactly equal to the number of clusters. Finally, our method unifies clustering and graph learning which can directly obtain cluster indicators from the graph itself without performing further clustering step. The effectiveness of this approach is examined on both single and multiple kernel learning scenarios in several datasets.

Connected through hyperedge HE-1 (induced by source paper "multi graph fusion for multi view spectral clustering"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "deep sparse subspace clustering"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "a survey on multi view clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "large scale multi view subspace clustering in linear time"):
  with members:
    - "robust graph learning from noisy data"
    - "multi view multiple clustering"
    - "spectral clustering via ensemble deep autoencoder learning sc edae"
    - "multiple partitions aligned clustering"
    - "efficient solvers for sparse subspace clustering"
    - "multi view low rank sparse subspace clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-3 (induced by source paper "latent multi view semi supervised classification"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "multiple partitions aligned clustering"
    - "a survey on multi view learning"

Connected through hyperedge HE-4 (induced by source paper "scaled simplex representation for subspace clustering"):
  with members:
    - "ell_0 motivated low rank sparse subspace clustering"
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "discriminative regression machine a classifier for high dimensional data or imbalanced data"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "discriminative transformation learning for fuzzy sparse subspace clustering"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"

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)