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: "a deterministic algorithm for maximizing submodular functions"
  Abstract: The problem of maximizing a non-negative submodular function was introduced by Feige, Mirrokni, and Vondrak [FOCS'07] who provided a deterministic local-search based algorithm that guarantees an approximation ratio of $\frac 1 3$, as well as a randomized $\frac 2 5$-approximation algorithm. An extensive line of research followed and various algorithms with improving approximation ratios were developed, all of them are randomized. Finally, Buchbinder et al. [FOCS'12] presented a randomized $\frac 1 2$-approximation algorithm, which is the best possible. #R##N#This paper gives the first deterministic algorithm for maximizing a non-negative submodular function that achieves an approximation ratio better than $\frac 1 3$. The approximation ratio of our algorithm is $\frac 2 5$. Our algorithm is based on recursive composition of solutions obtained by the local search algorithm of Feige et al. We show that the $\frac 2 5$ approximation ratio can be guaranteed when the recursion depth is $2$, and leave open the question of whether the approximation ratio improves as the recursion depth increases.

Connected through hyperedge HE-1 (induced by source paper "mixed robust average submodular partitioning fast algorithms guarantees and applications"):
  with members:
    - "graph partitioning via parallel submodular approximation to accelerate distributed machine learning"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "parallel training of dnns with natural gradient and parameter averaging"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "fast semidifferential based submodular function optimization"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "approximation algorithms for submodular multiway partition"
    - "submodular cost allocation problem and applications"

Connected through hyperedge HE-2 (induced by source paper "deterministic algorithms for submodular maximization problems"):
  with members:
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "streaming algorithms for submodular function maximization"
    - "online submodular maximization with preemption"
    - "comparing apples and oranges query tradeoff in submodular maximization"
    - "symmetry and approximability of submodular maximization problems"
    - "submodular maximization by simulated annealing"

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)