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 restless bandit problems"
  Abstract: The restless bandit problem is one of the most well-studied generalizations of the celebrated stochastic multi-armed bandit problem in decision theory. In its ultimate generality, the restless bandit problem is known to be PSPACE-Hard to approximate to any non-trivial factor, and little progress has been made despite its importance in modeling activity allocation under uncertainty. #R##N#We consider a special case that we call Feedback MAB, where the reward obtained by playing each of n independent arms varies according to an underlying on/off Markov process whose exact state is only revealed when the arm is played. The goal is to design a policy for playing the arms in order to maximize the infinite horizon time average expected reward. This problem is also an instance of a Partially Observable Markov Decision Process (POMDP), and is widely studied in wireless scheduling and unmanned aerial vehicle (UAV) routing. Unlike the stochastic MAB problem, the Feedback MAB problem does not admit to greedy index-based optimal policies. #R##N#We develop a novel and general duality-based algorithmic technique that yields a surprisingly simple and intuitive 2+epsilon-approximate greedy policy to this problem. We then define a general sub-class of restless bandit problems that we term Monotone bandits, for which our policy is a 2-approximation. Our technique is robust enough to handle generalizations of these problems to incorporate various side-constraints such as blocking plays and switching costs. This technique is also of independent interest for other restless bandit problems. By presenting the first (and efficient) O(1) approximations for non-trivial instances of restless bandits as well as of POMDPs, our work initiates the study of approximation algorithms in both these contexts.

Connected through hyperedge HE-1 (induced by source paper "regime switching bandits"):
  with members:
    - "a survey on practical applications of multi armed and contextual bandits"
    - "restless hidden markov bandits with linear rewards"
    - "introduction to multi armed bandits"
    - "learning to optimize under non stationarity"
    - "multi armed bandits for correlated markovian environments with smoothed reward feedback"
    - "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"

Connected through hyperedge HE-2 (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-3 (induced by source paper "rt byzcast byzantine resilient real time reliable broadcast"):
  with members:
    - "simple and efficient reliable broadcast in the presence of byzantine processes"

Connected through hyperedge HE-4 (induced by source paper "multi armed bandits with application to 5g small cells"):
  with members:
    - "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "approximation algorithms for bayesian multi armed bandit problems"):
  with members:
    - "active model selection"
    - "adaptive submodular optimization under matroid constraints"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "the ratio index for budgeted learning with applications"
    - "sequential design of experiments via linear programming"
    - "sponsored search auctions with markovian users"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-6 (induced by source paper "sufficient conditions on the optimality of myopic sensing in opportunistic channel access a unifying framework"):
  with members:
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "on optimality of myopic sensing policy with imperfect sensing in multi channel opportunistic access"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-7 (induced by source paper "polymatroid prophet inequalities"):
  with members:
    - "polyhedral clinching auctions and the adwords polytope"
    - "matroid prophet inequalities"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"

Connected through hyperedge HE-8 (induced by source paper "on the optimality of myopic sensing in multi state channels"):
  with members:
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

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)