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: "ell_0 motivated low rank sparse subspace clustering"
  Abstract: In many applications, high-dimensional data points can be well represented by low-dimensional subspaces. To identify the subspaces, it is important to capture a global and local structure of the data which is achieved by imposing low-rank and sparseness constraints on the data representation matrix. In low-rank sparse subspace clustering (LRSSC), nuclear and    ${\ell _{1}}$   -norms are used to measure rank and sparsity. However, the use of nuclear and    ${\ell _{1}}$   -norms leads to an overpenalized problem and only approximates the original problem. In this paper, we propose two    ${\ell _{0}}$    quasi-norm-based regularizations. First, this paper presents regularization based on multivariate generalization of minimax-concave penalty (GMC-LRSSC), which contains the global minimizers of a    ${\ell _{0}}$    quasi-norm regularized objective. Afterward, we introduce the Schatten-0 (   ${S_{0}}$   ) and    ${\ell _{0}}$   -regularized objective and approximate the proximal map of the joint solution using a proximal average method (   ${S_{0}/\ell _{0}}$   -LRSSC). The resulting nonconvex optimization problems are solved using an alternating direction method of multipliers with established convergence conditions of both algorithms. Results obtained on synthetic and four real-world datasets show the effectiveness of GMC-LRSSC and    ${S_{0}/\ell _{0}}$   -LRSSC when compared to state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "new phenomena in large scale internet traffic"):
  with members:
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "control principles of complex networks"
    - "technological networks and the spread of computer viruses"

Connected through hyperedge HE-2 (induced by source paper "hypersparse neural network analysis of large scale internet traffic"):
  with members:
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "technological networks and the spread of computer viruses"

Connected through hyperedge HE-3 (induced by source paper "scaled simplex representation for subspace clustering"):
  with members:
    - "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"
    - "clustering with similarity preserving"
    - "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)