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: "efficient l1 lq norm regularization"
  Abstract: Sparse learning has recently received increasing attentio n in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the `1=`q norm withq > 1 is attractive in many applications of regression and classification in that it facilitates group s parsity in the model. The resulting optimization problem is, however, challenging to solve due to the structure of the `1=`q-regularization. Existing work deals with special cases in cluding q = 2;1 , and they can not be easily extended to the general case. In this paper, we propose an efficient algorithm based on the accelerated grad ient method for solving the `1=`q-regularized problem, which is applicable for all values of q larger than 1, thus significantly extending existing work. One key buildi ng block of the proposed algorithm is the `1=`q-regularized Euclidean projection (EP1q). Our theoretical analysis reveals the key properties of EP1q and illustrates why EP1q for the general q is significantly more challenging to solve than the special c ases. Based on our theoretical analysis, we develop an efficient algorit hm for EP1q by solving two zero finding problems. Experimental results demonstrat e the efficiency of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "scalable bottom up subspace clustering using fp trees for high dimensional data"):
  with members:
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-2 (induced by source paper "efficient sparse subspace clustering by nearest neighbour filtering"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-3 (induced by source paper "sparse activity and sparse connectivity in supervised learning"):
  with members:
    - "multi column deep neural networks for image classification"
    - "deep big simple neural nets for handwritten digit recognition"
    - "comparing measures of sparsity"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-4 (induced by source paper "segmentation of subspaces in sequential data"):
  with members:
    - "robust subspace clustering"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "towards ultrahigh dimensional feature selection for big data"):
  with members:
    - "a feature selection method for multivariate performance measures"
    - "generalized fisher score for feature selection"
    - "feature hashing for large scale multitask learning"
    - "high dimensional non linear variable selection through hierarchical kernel learning"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-6 (induced by source paper "efficient euclidean projections onto the intersection of norm balls"):
  with members:
    - "optimization with sparsity inducing penalties"

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)