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 relaxed gradient support pursuit for sparsity constrained non convex optimization"):
  Abstract: Large-scale non-convex sparsity-constrained problems have recently gained extensive attention. Most existing deterministic optimization methods (e.g., GraSP) are not suitable for large-scale and high-dimensional problems, and thus stochastic optimization methods with hard thresholding (e.g., SVRGHT) become more attractive. Inspired by GraSP, this paper proposes a new general relaxed gradient support pursuit (RGraSP) framework, in which the sub-algorithm only requires to satisfy a slack descent condition. We also design two specific semi-stochastic gradient hard thresholding algorithms. In particular, our algorithms have much less hard thresholding operations than SVRGHT, and their average per-iteration cost is much lower (i.e., O(d) vs. O(d log(d)) for SVRGHT), which leads to faster convergence. Our experimental results on both synthetic and real-world datasets show that our algorithms are superior to the state-of-the-art gradient hard thresholding methods.

Includes members:
  Member "a simple stochastic variance reduced algorithm with fast convergence rates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive weight decay for deep neural networks")
  Member "tractable and scalable schatten quasi norm approximations for rank minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "factor group sparse regularization for efficient low rank matrix recovery")
  Member "direct acceleration of saga using sampled negative momentum":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop")
  Member "asvrg accelerated proximal svrg":
    also connected through other hyperedges:
      (none)
  Member "fast stochastic variance reduced gradient method with momentum acceleration for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quasi hyperbolic momentum and adam for deep learning")
  Member "scalable algorithms for tractable schatten quasi norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with deterministic sampling theories and methods")
  Member "stochastic variance reduced optimization for nonconvex sparse learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noodl provable online dictionary learning and sparse coding")
  Member "on variance reduction in stochastic gradient descent and its asynchronous variants":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster on device training using new federated momentum algorithm")

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)