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: "bandits with knapsacks"
  Abstract: Multi-armed bandit problems are the predominant theoretical model of exploration-exploitation tradeoffs in learning, and they have countless applications ranging from medical trials, to communication networks, to Web search and advertising. In many of these application domains the learner may be constrained by one or more supply (or budget) limits, in addition to the customary limitation on the time horizon. The literature lacks a general model encompassing these sorts of problems. We introduce such a model, called "bandits with knapsacks", that combines aspects of stochastic integer programming with online learning. A distinctive feature of our problem, in comparison to the existing regret-minimization literature, is that the optimal policy for a given latent distribution may significantly outperform the policy that plays the optimal fixed arm. Consequently, achieving sub linear regret in the bandits-with-knapsacks problem is significantly more challenging than in conventional bandit problems. We present two algorithms whose reward is close to the information-theoretic optimum: one is based on a novel "balanced exploration" paradigm, while the other is a primal-dual algorithm that uses multiplicative updates. Further, we prove that the regret achieved by both algorithms is optimal up to polylogarithmic factors. We illustrate the generality of the problem by presenting applications in a number of different domains including electronic commerce, routing, and scheduling. As one example of a concrete application, we consider the problem of dynamic posted pricing with limited supply and obtain the first algorithm whose regret, with respect to the optimal dynamic policy, is sub linear in the supply.

Connected through hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

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 "advances in bandits with knapsacks"):
  with members:
    - "introduction to multi armed bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "adversarial bandits with knapsacks"
    - "combinatorial semi bandits with knapsacks"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "efficient learning in large scale combinatorial semi bandits"
    - "resourceful contextual bandits"

Connected through hyperedge HE-4 (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"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-5 (induced by source paper "budget constrained bandits over general cost and reward distributions"):
  with members:
    - "combinatorial semi bandits with knapsacks"
    - "thompson sampling for budgeted multi armed bandits"
    - "linear contextual bandits with knapsacks"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "a farewell to arms sequential reward maximization on a budget with a giving up option"):
  with members:
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "online learning in repeated auctions"
    - "online learning under delayed feedback"
    - "knapsack based optimal policies for budget limited multi armed bandits"

Connected through hyperedge HE-7 (induced by source paper "online assortment with limited inventories and set dependent revenues"):
  with members:
    - "optimal search segmentation mechanisms for online platform markets"
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "multi stage and multi customer assortment optimization with inventory constraints"
    - "how the experts algorithm can help solve lps online"

Connected through hyperedge HE-8 (induced by source paper "learning in markov decision processes under constraints"):
  with members:
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

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)