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 "provable non convex optimization and algorithm validation via submodularity"):
  Abstract: Submodularity is one of the most well-studied properties of problem classes in combinatorial optimization and many applications of machine learning and data mining, with strong implications for guaranteed optimization. In this thesis, we investigate the role of submodularity in provable non-convex optimization and validation of algorithms. A profound understanding which classes of functions can be tractably optimized remains a central challenge for non-convex optimization. By advancing the notion of submodularity to continuous domains (termed "continuous submodularity"), we characterize a class of generally non-convex and non-concave functions -- continuous submodular functions, and derive algorithms for approximately maximizing them with strong approximation guarantees. Meanwhile, continuous submodularity captures a wide spectrum of applications, ranging from revenue maximization with general marketing strategies, MAP inference for DPPs to mean field inference for probabilistic log-submodular models, which renders it as a valuable domain knowledge in optimizing this class of objectives. Validation of algorithms is an information-theoretic framework to investigate the robustness of algorithms to fluctuations in the input/observations and their generalization ability. We investigate various algorithms for one of the paradigmatic unconstrained submodular maximization problem: MaxCut. Due to submodularity of the MaxCut objective, we are able to present efficient approaches to calculate the algorithmic information content of MaxCut algorithms. The results provide insights into the robustness of different algorithmic techniques for MaxCut.

Includes members:
  Member "continuous dr submodular maximization structure and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "non monotone dr submodular maximization approximation and regret guarantees")
  Member "greedy maxcut algorithms and their information content":
    also connected through other hyperedges:
      (none)
  Member "beyond convexity stochastic quasi convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "scalable variational inference in log supermodular models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexible modeling of diversity with strongly log concave distributions")
  Member "maximizing monotone submodular functions over the 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 "non negative principal component analysis message passing algorithms and sharp asymptotics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective")
  Member "selecting the rank of truncated svd by maximum approximation capacity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reaching data confidentiality and model accountability on the caltrain")

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)