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: "scenario submodular cover"
  Abstract: Many problems in Machine Learning can be modeled as submodular optimization problems. Recent work has focused on stochastic or adaptive versions of these problems. We consider the Scenario Submodular Cover problem, which is a counterpart to the Stochastic Submodular Cover problem studied by Golovin and Krause. In Scenario Submodular Cover, the goal is to produce a cover with minimum expected cost, where the expectation is with respect to an empirical joint distribution, given as input by a weighted sample of realizations. In contrast, in Stochastic Submodular Cover, the variables of the input distribution are assumed to be independent, and the distribution of each variable is given as input. Building on algorithms developed by Cicalese et al. and Golovin and Krause for related problems, we give two approximation algorithms for Scenario Submodular Cover over discrete distributions. The first achieves an approximation factor of O(log Qm), where m is the size of the sample and Q is the goal utility. The second, simpler algorithm achieves an approximation bound of O(log QW), where Q is the goal utility and W is the sum of the integer weights. (Both bounds assume an integer-valued utility function.) Our results yield approximation bounds for other problems involving non-independent distributions that are explicitly specified by their support.

Connected through hyperedge HE-1 (induced by source paper "stochastic submodular cover with limited adaptivity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "stochastic packing integer programs with few queries"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-2 (induced by source paper "the stochastic score classification problem"):
  with members:
    - "submodular goal value of boolean functions"
    - "optimal computation of symmetric boolean functions in collocated networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "adaptive submodular ranking"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "near optimal bayesian active learning for decision making"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "analytical approach to parallel repetition"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)