human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "near optimal approximate discrete and continuous submodular function minimization"
  Abstract: In this paper we provide improved running times and oracle complexities for approximately minimizing a submodular function. Our main result is a randomized algorithm, which given any submodular function defined on $n$-elements with range $[-1, 1]$, computes an $\epsilon$-additive approximate minimizer in $\tilde{O}(n/\epsilon^2)$ oracle evaluations with high probability. This improves over the $\tilde{O}(n^{5/3}/\epsilon^2)$ oracle evaluation algorithm of Chakrabarty \etal~(STOC 2017) and the $\tilde{O}(n^{3/2}/\epsilon^2)$ oracle evaluation algorithm of Hamoudi \etal. #R##N#Further, we leverage a generalization of this result to obtain efficient algorithms for minimizing a broad class of nonconvex functions. For any function $f$ with domain $[0, 1]^n$ that satisfies $\frac{\partial^2f}{\partial x_i \partial x_j} \le 0$ for all $i \neq j$ and is $L$-Lipschitz with respect to the $L^\infty$-norm we give an algorithm that computes an $\epsilon$-additive approximate minimizer with $\tilde{O}(n \cdot \mathrm{poly}(L/\epsilon))$ function evaluation with high probability.

Connected through hyperedge HE-1 (induced by source paper "optimal approximation for unconstrained non submodular minimization"):
  with members:
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"
    - "gradient methods for submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "structured sparsity discrete and convex approaches"

Connected through hyperedge HE-2 (induced by source paper "quantum and classical algorithms for approximate submodular function minimization"):
  with members:
    - "subquadratic submodular function minimization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "learning with submodular functions a convex optimization perspective"
    - "sublinear optimization for machine learning"

Question: Please tell me which class the center node 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)