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 "distributed convolutional sparse coding"):
  Abstract: We consider the problem of building shift-invariant representations for long signals in the context of distributed processing. We propose an asynchronous algorithm based on coordinate descent called DICOD to efficiently solve the $\ell_1$-minimization problems involved in convolutional sparse coding. This algorithm leverages the weak temporal dependency of the convolution to reduce the interprocess communication to a few local messages. We prove that this algorithm converges to the optimal solution and that it scales with superlinear speedup, up to a certain limit. These properties are illustrated with numerical experiments and our algorithm is compared to the state-of-the-art methods used for convolutional sparse coding.

Includes members:
  Member "working locally thinking globally theoretical guarantees for convolutional sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding")
  Member "scaling up coordinate descent algorithms for large ell_1 regularization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a parallel and efficient algorithm for learning to match")
  Member "parallel coordinate descent for l1 regularized loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning systems for highly distributed and rapidly growing data")
  Member "safe feature elimination for the lasso and sparse supervised learning problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe screening for the generalized conditional gradient method")

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)