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: "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
  Abstract: Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. #R##N#We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). #R##N#Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.

Connected through hyperedge HE-1 (induced by source paper "the complexity of contracts"):
  with members:
    - "delegated search approximates efficient search"
    - "a duality based unified approach to bayesian mechanism design"
    - "simple versus optimal contracts"
    - "bernoulli factories and black box reductions in mechanism design"
    - "the menu size complexity of revenue approximation"
    - "algorithmic bayesian persuasion"
    - "mixture selection mechanism design and signaling"
    - "non optimal mechanism design"

Connected through hyperedge HE-2 (induced by source paper "incentivizing exploration with selective data disclosure"):
  with members:
    - "sample complexity of incentivized exploration"
    - "introduction to multi armed bandits"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "optimal and myopic information acquisition"
    - "descending price optimally coordinates search"
    - "bayesian incentive compatible bandit exploration"

Connected through hyperedge HE-3 (induced by source paper "sample complexity of incentivized exploration"):
  with members:
    - "introduction to multi armed bandits"
    - "overabundant information and learning traps"
    - "optimal and myopic information acquisition"
    - "descending price optimally coordinates search"
    - "bayesian incentive compatible bandit exploration"
    - "economic recommendation systems"
    - "bandits with knapsacks"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-4 (induced by source paper "bayesian exploration with heterogeneous agents"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "incentivizing exploration with unbiased histories"
    - "competing bandits learning under competition"
    - "fairness incentives for myopic agents"
    - "optimal and myopic information acquisition"
    - "selling to a no regret buyer"

Connected through hyperedge HE-5 (induced by source paper "the perils of exploration under competition a computational modeling approach"):
  with members:
    - "competing bandits learning under competition"
    - "a tutorial on thompson sampling"
    - "selling to a no regret buyer"
    - "making contextual decisions with low technical debt"
    - "bayesian incentive compatible bandit exploration"
    - "learning prices for repeated auctions with strategic buyers"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "truthful mechanisms with implicit payment computation"

Connected through hyperedge HE-6 (induced by source paper "adversarial contract design for private data commercialization"):
  with members:
    - "blockchain enabled data marketplace design and challenges"
    - "implementation of smart contracts using hybrid architectures with on and off blockchain components"
    - "contract design for energy demand response"
    - "market model and optimal pricing scheme of big data and internet of things iot"
    - "buying private data without verification"
    - "differential privacy an economic method for choosing epsilon"
    - "a theory of pricing private data"
    - "privacy aware mechanism design"

Connected through hyperedge HE-7 (induced by source paper "multi armed bandits for correlated markovian environments with smoothed reward feedback"):
  with members:
    - "bandit learning with positive externalities"
    - "discrepancy based algorithms for non stationary rested bandits"
    - "a multi armed bandit approach for online expert selection in markov decision processes"
    - "collaborative filtering bandits"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "regret bounds for restless markov bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "incentivizing the dynamic workforce learning contracts in the gig economy"):
  with members:
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sample complexity of automated mechanism design"
    - "lipschitz bandits regret lower bounds and optimal algorithms"
    - "bandits and experts in metric spaces"

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)