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: "approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover"
  Abstract: Stochastic Boolean Function Evaluation is the problem of determining the value of a given Boolean function f on an unknown input x, when each bit of x_i of x can only be determined by paying an associated cost c_i. The assumption is that x is drawn from a given product distribution, and the goal is to minimize the expected cost. This problem has been studied in Operations Research, where it is known as "sequential testing" of Boolean functions. It has also been studied in learning theory in the context of learning with attribute costs. We consider the general problem of developing approximation algorithms for Stochastic Boolean Function Evaluation. We give a 3-approximation algorithm for evaluating Boolean linear threshold formulas. We also present an approximation algorithm for evaluating CDNF formulas (and decision trees) achieving a factor of O(log kd), where k is the number of terms in the DNF formula, and d is the number of clauses in the CNF formula. In addition, we present approximation algorithms for simultaneous evaluation of linear threshold functions, and for ranking of linear functions. #R##N#Our function evaluation algorithms are based on reductions to the Stochastic Submodular Set Cover (SSSC) problem. This problem was introduced by Golovin and Krause. They presented an approximation algorithm for the problem, called Adaptive Greedy. Our main technical contribution is a new approximation algorithm for the SSSC problem, which we call Adaptive Dual Greedy. It is an extension of the Dual Greedy algorithm for Submodular Set Cover due to Fujito, which is a generalization of Hochbaum's algorithm for the classical Set Cover Problem. We also give a new bound on the approximation achieved by the Adaptive Greedy algorithm of Golovin and Krause.

Connected through hyperedge HE-1 (induced by source paper "near optimal adaptivity gaps for stochastic multi value probing"):
  with members:
    - "a ptas for a class of stochastic dynamic programs"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "combinatorial prophet inequalities"
    - "discrete stochastic submodular maximization adaptive vs non adaptive vs offline"
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"
    - "on the adaptivity gap of stochastic orienteering"
    - "a stochastic probing problem with applications"

Connected through hyperedge HE-2 (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"
    - "scenario submodular cover"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "parallel algorithms for select and partition with noisy comparisons"

Connected through hyperedge HE-3 (induced by source paper "scenario submodular cover"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "adaptive submodular ranking"
    - "near optimal bayesian active learning for decision making"
    - "a note on the generalized min sum set cover problem"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "decision trees for function evaluation simultaneous optimization of worst and expected cost"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "evaluation of dnf formulas"
    - "near optimal bayesian active learning with noisy observations"
    - "interactive submodular set cover"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"

Connected through hyperedge HE-5 (induced by source paper "evaluation of dnf formulas"):
  with members:
    - "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)