human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "average case analysis of multichannel sparse recovery using convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")
  Member "sparse online learning via truncated gradient":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "full deep neural network training on a pruned weight budget")
  Member "consistency of the group lasso and multiple kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint user selection and precoding in multiuser mimo systems via group lasso")

Question: Please tell me which class the source paper 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)