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 "optimal dr submodular maximization and applications to provable mean field inference"):
  Abstract: Mean field inference in probabilistic models is generally a highly nonconvex problem. Existing optimization methods, e.g., coordinate ascent algorithms, can only generate local optima. #R##N#In this work we propose provable mean filed methods for probabilistic log-submodular models and its posterior agreement (PA) with strong approximation guarantees. The main algorithmic technique is a new Double Greedy scheme, termed DR-DoubleGreedy, for continuous DR-submodular maximization with box-constraints. It is a one-pass algorithm with linear time complexity, reaching the optimal 1/2 approximation ratio, which may be of independent interest. We validate the superior performance of our algorithms against baseline algorithms on both synthetic and real-world datasets.

Includes members:
  Member "optimal algorithms for continuous non monotone submodular and dr submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online non monotone dr submodular maximization")
  Member "guarantees for greedy maximization of non submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "robust budget allocation via continuous submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "non monotone dr submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non monotone dr submodular maximization approximation and regret guarantees")
  Member "guaranteed non convex optimization submodular maximization over continuous domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online policies for efficient volunteer crowdsourcing")
  Member "a reduction for optimizing lattice submodular functions with diminishing returns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "submodular function maximization on the bounded integer lattice":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "majorisation minimisation algorithms for minimising the difference between lattice submodular functions")

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)