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: "similarity learning via kernel preserving embedding"
  Abstract: Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semi-supervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-of-the-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.

Connected through hyperedge HE-1 (induced by source paper "outlier detection ensemble with embedded feature selection"):
  with members:
    - "robust graph learning from noisy data"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "feature selection a data perspective"
    - "sequential ensemble learning for outlier detection a bias variance perspective"

Connected through hyperedge HE-2 (induced by source paper "tensor decomposition with relational constraints for predicting multiple types of microrna disease associations"):
  with members:
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-3 (induced by source paper "structure learning with similarity preserving"):
  with members:
    - "multi view multiple clustering"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep subspace clustering networks"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "top n recommender system via matrix completion"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-4 (induced by source paper "clustering with similarity preserving"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "structured low rank matrix factorization global optimality algorithms and applications"
    - "kernel spectral clustering and applications"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-5 (induced by source paper "robust graph learning from noisy data"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "connections between nuclear norm and frobenius norm based representations"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust principal component analysis on graphs"
    - "unsupervised feature selection with adaptive structure learning"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-6 (induced by source paper "multiple partitions aligned clustering"):
  with members:
    - "robust graph learning from noisy data"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"
    - "a survey on multi view clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "predicting microrna disease associations from knowledge graph using tensor decomposition with relational constraints"):
  with members:
    - "tucker tensor factorization for knowledge graph completion"
    - "tensorly tensor learning in python"
    - "large scale multi label learning with missing labels"

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)