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: "unified spectral clustering with optimal graph"
  Abstract: Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to severe information loss and performance degradation. First, predefined similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a unified framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.

Connected through hyperedge HE-1 (induced by source paper "multi view subspace clustering via partition fusion"):
  with members:
    - "robust graph learning from noisy data"
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "multi view multiple clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "exploring graph learning for semi supervised classification beyond euclidean data"
    - "multiple partitions aligned clustering"
    - "multiview spectral clustering via structured low rank matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "clustering with similarity preserving"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "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-3 (induced by source paper "lifelong spectral clustering"):
  with members:
    - "representative task self selection for flexible clustered lifelong learning"
    - "lifelong domain word embedding via meta learning"
    - "lifelong learning for sentiment classification"
    - "selective experience replay for lifelong learning"
    - "encoder based lifelong learning"
    - "learning without forgetting"

Connected through hyperedge HE-4 (induced by source paper "multiple partitions aligned clustering"):
  with members:
    - "robust graph learning from noisy data"
    - "similarity learning via kernel preserving embedding"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "joint learning of self representation and indicator for multi view image clustering"):
  with members:
    - "fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion"
    - "on unifying multi view self representations for clustering by tensor multi rank minimization"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-6 (induced by source paper "similarity learning via kernel preserving embedding"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unsupervised feature selection with adaptive structure learning"

Connected through hyperedge HE-7 (induced by source paper "discrete optimal graph clustering"):
  with members:
    - "a generalized power iteration method for solving quadratic problem on the stiefel manifold"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-8 (induced by source paper "the spectacl of nonconvex clustering a spectral approach to density based clustering"):
  with members:
    - "on the complexity of nonnegative matrix factorization"

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)