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: "on the adaptivity gap of stochastic orienteering"
  Abstract: The input to the stochastic orienteering problem consists of a budget $B$ and metric $(V,d)$ where each vertex $v$ has a job with deterministic reward and random processing time (drawn from a known distribution). The processing times are independent across vertices. The goal is to obtain a non-anticipatory policy to run jobs at different vertices, that maximizes expected reward, subject to the total distance traveled plus processing times being at most $B$. An adaptive policy is one that can choose the next vertex to visit based on observed random instantiations. Whereas, a non-adaptive policy is just given by a fixed ordering of vertices. The adaptivity gap is the worst-case ratio of the expected rewards of the optimal adaptive and non-adaptive policies. #R##N#We prove an $\Omega(\log\log B)^{1/2}$ lower bound on the adaptivity gap of stochastic orienteering. This provides a negative answer to the $O(1)$ adaptivity gap conjectured earlier, and comes close to the $O(\log\log B)$ upper bound. This result holds even on a line metric. #R##N#We also show an $O(\log\log B)$ upper bound on the adaptivity gap for the correlated stochastic orienteering problem, where the reward of each job is random and possibly correlated to its processing time. Using this, we obtain an improved quasi-polynomial time approximation algorithm for correlated stochastic orienteering.

Connected through hyperedge HE-1 (induced by source paper "the markovian price of information"):
  with members:
    - "the price of information in combinatorial optimization"
    - "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"

Connected through hyperedge HE-2 (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"
    - "approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"

Connected through hyperedge HE-3 (induced by source paper "the price of information in combinatorial optimization"):
  with members:
    - "descending price coordinates approximately efficient search"
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "stochastic combinatorial optimization via poisson approximation"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-4 (induced by source paper "adaptivity gaps for stochastic probing submodular and xos functions"):
  with members:
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "stochastic combinatorial optimization via poisson approximation"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"

Connected through hyperedge HE-5 (induced by source paper "approximation algorithms for stochastic k tsp"):
  with members:
    - "a stochastic probing problem with applications"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "incremental medians via online bidding"

Connected through hyperedge HE-6 (induced by source paper "improvements and generalizations of stochastic knapsack and multi armed bandit approximation algorithms full version"):
  with members:
    - "approximation algorithms for bayesian multi armed bandit problems"
    - "stochastic combinatorial optimization via poisson approximation"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "sequential design of experiments via linear programming"

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)