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 approximation for unconstrained non submodular minimization"):
  Abstract: Submodular function minimization is a well studied problem; existing algorithms solve it exactly or up to arbitrary accuracy. However, in many applications, the objective function is not exactly submodular. No theoretical guarantees exist in this case. While submodular minimization algorithms rely on intricate connections between submodularity and convexity, we show that these relations can be extended sufficiently to obtain approximation guarantees for non-submodular minimization. In particular, we prove how a projected subgradient method can perform well even for certain non-submodular functions. #R##N#This includes important examples, such as objectives for structured sparse learning and variance reduction in Bayesian optimization. We also extend this result to noisy function evaluations. Our algorithm works in the value oracle model. We prove that in this model, the approximation result we obtain is the best possible with a subexponential number of queries.

Includes members:
  Member "near optimal approximate discrete and continuous submodular function minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantum and classical algorithms for approximate submodular function minimization")
  Member "fast maximization of non submodular monotonic functions on the integer lattice":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "submodular maximization beyond non negativity guarantees fast algorithms and applications")
  Member "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness":
    also connected through other hyperedges:
      (none)
  Member "gradient methods for submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "guarantees for greedy maximization of non submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond submodular maximization")
  Member "submodular optimization under noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms")
  Member "subquadratic submodular function minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "tolerant junta testing and the connection to submodular optimization and function isomorphism":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating the distance to monotonicity of boolean 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)