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 "guaranteed non convex optimization submodular maximization over continuous domains"):
  Abstract: Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation guarantees. Specifically, i) We introduce the weak DR property that gives a unified characterization of submodularity for all set, integer-lattice and continuous functions; ii) for maximizing monotone DR-submodular continuous functions under general down-closed convex constraints, we propose a Frank-Wolfe variant with $(1-1/e)$ approximation guarantee, and sub-linear convergence rate; iii) for maximizing general non-monotone submodular continuous functions subject to box constraints, we propose a DoubleGreedy algorithm with $1/3$ approximation guarantee. Submodular continuous functions naturally find applications in various real-world settings, including influence and revenue maximization with continuous assignments, sensor energy management, multi-resolution data summarization, facility location, etc. Experimental results show that the proposed algorithms efficiently generate superior solutions compared to baseline algorithms.

Includes members:
  Member "deep submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "powerset convolutional neural networks")
  Member "a reduction for optimizing lattice submodular functions with diminishing returns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "majorisation minimisation algorithms for minimising the difference between lattice submodular functions")
  Member "submodular functions from discrete to continous domains":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "on graduated optimization for stochastic non convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "submodular function maximization on the bounded integer lattice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "majorisation minimisation algorithms for minimising the difference between lattice submodular functions")
  Member "maximizing monotone submodular functions over the integer lattice":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable non convex optimization and algorithm validation via submodularity")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "near optimal nonmyopic value of information in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")

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)