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 "subquadratic submodular function minimization"):
  Abstract: Submodular function minimization (SFM) is a fundamental discrete optimization problem which generalizes many well known problems, has applications in various fields, and can be solved in polynomial time. Owing to applications in computer vision and machine learning, fast SFM algorithms are highly desirable. The current fastest algorithms [Lee, Sidford, Wong, FOCS 2015] run in $O(n^{2}\log nM\cdot\textrm{EO} +n^{3}\log^{O(1)}nM)$ time and $O(n^{3}\log^{2}n\cdot \textrm{EO} +n^{4}\log^{O(1)}n$) time respectively, where $M$ is the largest absolute value of the function (assuming the range is integers) and $\textrm{EO}$ is the time taken to evaluate the function on any set. Although the best known lower bound on the query complexity is only $\Omega(n)$, the current shortest non-deterministic proof certifying the optimum value of a function requires $\Theta(n^{2})$ function evaluations. #R##N#The main contribution of this paper are subquadratic SFM algorithms. For integer-valued submodular functions, we give an SFM algorithm which runs in $O(nM^{3}\log n\cdot\textrm{EO})$ time giving the first nearly linear time algorithm in any known regime. For real-valued submodular functions with range in $[-1,1]$, we give an algorithm which in $\tilde{O}(n^{5/3}\cdot\textrm{EO}/\varepsilon^{2})$ time returns an $\varepsilon$-additive approximate solution. At the heart of it, our algorithms are projected stochastic subgradient descent methods on the Lovasz extension of submodular functions where we crucially exploit submodularity and data structures to obtain fast, i.e. sublinear time subgradient updates. . The latter is crucial for beating the $n^{2}$ bound as we show that algorithms which access only subgradients of the Lovasz extension, and these include the theoretically best algorithms mentioned above, must make $\Omega(n)$ subgradient calls (even for functions whose range is $\{-1,0,1\}$).

Includes members:
  Member "random coordinate descent methods for minimizing decomposable submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds")
  Member "a faster cutting plane method and its implications for combinatorial and convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "tight bounds for approximate carath eodory and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "provable submodular minimization using wolfe s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quadratic decomposable submodular function minimization theory and practice")
  Member "submodular meets structured finding diverse subsets in exponentially large structured item sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "diversity in machine learning")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "curvature and optimal algorithms for learning and minimizing submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "reflection methods for user friendly submodular optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "quadratic decomposable submodular function minimization theory and practice")

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.DS(Data Structures and Algorithms)