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 correlated knapsacks and non martingale bandits"
  Abstract: In the stochastic knapsack problem, we are given a knapsack of size B, and a set of jobs whose sizes and rewards are drawn from a known probability distribution. However, we know the actual size and reward only when the job completes. How should we schedule jobs to maximize the expected total reward? We know O(1)-approximations when we assume that (i) rewards and sizes are independent random variables, and (ii) we cannot prematurely cancel jobs. What can we say when either or both of these assumptions are changed? #R##N#The stochastic knapsack problem is of interest in its own right, but techniques developed for it are applicable to other stochastic packing problems. Indeed, ideas for this problem have been useful for budgeted learning problems, where one is given several arms which evolve in a specified stochastic fashion with each pull, and the goal is to pull the arms a total of B times to maximize the reward obtained. Much recent work on this problem focus on the case when the evolution of the arms follows a martingale, i.e., when the expected reward from the future is the same as the reward at the current state. What can we say when the rewards do not form a martingale? #R##N#In this paper, we give constant-factor approximation algorithms for the stochastic knapsack problem with correlations and/or cancellations, and also for budgeted learning problems where the martingale condition is not satisfied. Indeed, we can show that previously proposed LP relaxations have large integrality gaps. We propose new time-indexed LP relaxations, and convert the fractional solutions into distributions over strategies, and then use the LP values and the time ordering information from these strategies to devise a randomized adaptive scheduling algorithm. We hope our LP formulation and decomposition methods may provide a new way to address other correlated bandit problems with more general contexts.

Connected through hyperedge HE-1 (induced by source paper "predict and match prophet inequalities with uncertain supply"):
  with members:
    - "prophet secretary through blind strategies"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "prophet secretary for combinatorial auctions and matroids"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "combinatorial prophet inequalities"
    - "combinatorial auctions via posted prices"
    - "matroid prophet inequalities"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"

Connected through hyperedge HE-2 (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-3 (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"
    - "on the adaptivity gap of stochastic orienteering"

Connected through hyperedge HE-4 (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"
    - "the price of information in combinatorial optimization"
    - "descending price coordinates approximately efficient search"
    - "a stochastic probing problem with applications"

Connected through hyperedge HE-5 (induced by source paper "stochastic submodular probing with state dependent costs"):
  with members:
    - "submodular stochastic probing on matroids"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "hiring under uncertainty"):
  with members:
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "improved approximation algorithms for stochastic matching"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-7 (induced by source paper "an fptas for stochastic unbounded min knapsack problem"):
  with members:
    - "a faster fptas for the unbounded knapsack problem"
    - "a fully polynomial time approximation scheme for approximating a sum of random variables"
    - "stochastic combinatorial optimization via poisson approximation"

Connected through hyperedge HE-8 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

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)