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: "the price of information in combinatorial optimization"
  Abstract: Consider a network design application where we wish to lay down a minimum-cost spanning tree in a given graph; however, we only have stochastic information about the edge costs. To learn the precise cost of any edge, we have to conduct a study that incurs a price. Our goal is to find a spanning tree while minimizing the disutility, which is the sum of the tree cost and the total price that we spend on the studies. In a different application, each edge gives a stochastic reward value. Our goal is to find a spanning tree while maximizing the utility, which is the tree reward minus the prices that we pay. #R##N#Situations such as the above two often arise in practice where we wish to find a good solution to an optimization problem, but we start with only some partial knowledge about the parameters of the problem. The missing information can be found only after paying a probing price, which we call the price of information. What strategy should we adopt to optimize our expected utility/disutility? #R##N#A classical example of the above setting is Weitzman's "Pandora's box" problem where we are given probability distributions on values of $n$ independent random variables. The goal is to choose a single variable with a large value, but we can find the actual outcomes only after paying a price. Our work is a generalization of this model to other combinatorial optimization problems such as matching, set cover, facility location, and prize-collecting Steiner tree. We give a technique that reduces such problems to their non-price counterparts, and use it to design exact/approximation algorithms to optimize our utility/disutility. Our techniques extend to situations where there are additional constraints on what parameters can be probed or when we can simultaneously probe a subset of the parameters.

Connected through hyperedge HE-1 (induced by source paper "pandora s box problem with order constraints"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "pandora s problem with nonobligatory inspection"
    - "generalizing complex hypotheses on product distributions auctions prophet inequalities and pandora s problem"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "guess free maximization of submodular and linear sums"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-2 (induced by source paper "a simple 1 1 e approximation for oblivious bipartite matching"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "perturbed greedy on oblivious matching problems"
    - "how to match when all vertices arrive online"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-3 (induced by source paper "learning optimal search algorithms from data"):
  with members:
    - "learning to prune speeding up repeated computations"
    - "the markovian price of information"
    - "learning to branch"
    - "competitive caching with machine learned advice"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"

Connected through hyperedge HE-4 (induced by source paper "the markovian price of information"):
  with members:
    - "optimal data acquisition for statistical estimation"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "descending price optimally coordinates search"
    - "descending price coordinates approximately efficient search"
    - "online contention resolution schemes"
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"
    - "on the adaptivity gap of stochastic orienteering"

Connected through hyperedge HE-5 (induced by source paper "beating greedy for stochastic bipartite matching"):
  with members:
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "prophet secretary for combinatorial auctions and matroids"
    - "almost optimal stochastic weighted matching with few queries"
    - "stochastic packing integer programs with few queries"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "improved approximation algorithms for stochastic matching"
    - "the query commit problem"

Connected through hyperedge HE-6 (induced by source paper "algorithms and adaptivity gaps for stochastic k tsp"):
  with members:
    - "an fptas for stochastic unbounded min knapsack problem"
    - "a ptas for a class of stochastic dynamic programs"
    - "descending price coordinates approximately efficient search"
    - "a stochastic probing problem with applications"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"

Connected through hyperedge HE-7 (induced by source paper "submodular stochastic probing with prices"):
  with members:
    - "random order contention resolution schemes"
    - "online contention resolution schemes"
    - "non negative submodular stochastic probing via stochastic contention resolution schemes"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "online stochastic matching beating 1 1 e"

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)