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: "characterizing truthful multi armed bandit mechanisms"
  Abstract: We consider a multi-round auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare. #R##N#If the advertisers bid their true private values, our problem is equivalent to the "multi-armed bandit problem", and thus can be viewed as a strategic version of the latter. In particular, for both problems the quality of an algorithm can be characterized by "regret", the difference in social welfare between the algorithm and the benchmark which always selects the same "best" advertisement. We investigate how the design of multi-armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful. We find that truthful mechanisms have certain strong structural properties -- essentially, they must separate exploration from exploitation -- and they incur much higher regret than the optimal multi-armed bandit algorithms. Moreover, we provide a truthful mechanism which (essentially) matches our lower bound on regret.

Connected through hyperedge HE-1 (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"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandits with knapsacks"

Connected through hyperedge HE-2 (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-3 (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"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "learning prices for repeated auctions with strategic buyers"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "a dominant strategy truthful deterministic multi armed bandit mechanism with logarithmic regret"):
  with members:
    - "truthful learning mechanisms for multi slot sponsored search auctions with externalities"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "truthful mechanisms with implicit payment computation"

Connected through hyperedge HE-5 (induced by source paper "budget constrained multi armed bandits with multiple plays"):
  with members:
    - "bandits with knapsacks"
    - "selective call out and real time bidding"

Connected through hyperedge HE-6 (induced by source paper "bayesian incentive compatible bandit exploration"):
  with members:
    - "fairness in learning classic and contextual bandits"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "economic recommendation systems"
    - "online learning with feedback graphs beyond bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandits with knapsacks"
    - "from bandits to experts a tale of domination and independence"

Connected through hyperedge HE-7 (induced by source paper "bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "linear contextual bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "adaptive crowdsourcing algorithms for the bandit survey problem"
    - "knapsack based optimal policies for budget limited multi armed bandits"

Connected through hyperedge HE-8 (induced by source paper "bandits and experts in metric spaces"):
  with members:
    - "bandits with concave rewards and convex knapsacks"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandit algorithms for tree search"
    - "bandits with knapsacks"
    - "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"
    - "truthful mechanisms with implicit payment computation"

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)