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 "a memoization framework for scaling submodular optimization to large scale problems"):
  Abstract: We are motivated by large scale submodular optimization problems, where standard algorithms that treat the submodular functions in the \emph{value oracle model} do not scale. In this paper, we present a model called the \emph{precomputational complexity model}, along with a unifying memoization based framework, which looks at the specific form of the given submodular function. A key ingredient in this framework is the notion of a \emph{precomputed statistic}, which is maintained in the course of the algorithms. We show that we can easily integrate this idea into a large class of submodular optimization problems including constrained and unconstrained submodular maximization, minimization, difference of submodular optimization, optimization with submodular constraints and several other related optimization problems. Moreover, memoization can be integrated in both discrete and continuous relaxation flavors of algorithms for these problems. We demonstrate this idea for several commonly occurring submodular functions, and show how the precomputational model provides significant speedups compared to the value oracle model. Finally, we empirically demonstrate this for large scale machine learning problems of data subset selection and summarization.

Includes members:
  Member "stochastic submodular maximization the case of coverage functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end learning and optimization on graphs")
  Member "gradient methods for submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "deep submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "powerset convolutional neural networks")
  Member "submodular optimization under noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms")
  Member "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "provable submodular minimization using wolfe s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quadratic decomposable submodular function minimization theory and practice")
  Member "lazier than lazy greedy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust submodular minimization with applications to cooperative modeling")

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)