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: "whittle index policy for crawling ephemeral content"
  Abstract: We consider a task of scheduling a crawler to retrieve content from several sites with ephemeral content. A user typically loses interest in ephemeral content, like news or posts at social network groups, after several days or hours. Thus, development of timely crawling policy for such ephemeral information sources is very important. We first formulate this problem as an optimal control problem with average reward. The reward can be measured in the number of clicks or relevant search requests. The problem in its initial formulation suffers from the curse of dimensionality and quickly becomes intractable even with moderate number of information sources. Fortunately, this problem admits a Whittle index, which leads to problem decomposition and to a very simple and efficient crawling policy. We derive the Whittle index and provide its theoretical justification.

Connected through hyperedge HE-1 (induced by source paper "constrained restless bandits for dynamic scheduling in cyber physical systems"):
  with members:
    - "sequential decision making under uncertainty with dynamic resource constraints"
    - "sequential decision making with limited observation capability application to wireless networks"
    - "a hidden markov restless multi armed bandit model for playout recommendation systems"
    - "asymptotically optimal downlink scheduling over markovian fading channels"

Connected through hyperedge HE-2 (induced by source paper "sequential decision making under uncertainty with dynamic resource constraints"):
  with members:
    - "sequential decision making with limited observation capability application to wireless networks"
    - "a hidden markov restless multi armed bandit model for playout recommendation systems"
    - "asymptotically optimal downlink scheduling over markovian fading channels"

Connected through hyperedge HE-3 (induced by source paper "a hidden markov restless multi armed bandit model for playout recommendation systems"):
  with members:
    - "on the whittle index for restless multi armed hidden markov bandits"
    - "optimal recommendation to users that react online learning for a class of pomdps"
    - "leveraging side observations in stochastic bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-4 (induced by source paper "opportunistic scheduling as restless bandits"):
  with members:
    - "whittle indexability in egalitarian processor sharing systems"

Connected through hyperedge HE-5 (induced by source paper "on the whittle index for restless multi armed hidden markov bandits"):
  with members:
    - "congestion control of tcp flows in internet routers by means of index policy"
    - "on optimality of myopic sensing policy with imperfect sensing in multi channel opportunistic access"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "joint design and separation principle for opportunistic spectrum access in the presence of sensing errors"

Connected through hyperedge HE-6 (induced by source paper "optimal recommendation to users that react online learning for a class of pomdps"):
  with members:
    - "on the whittle index for restless multi armed hidden markov bandits"
    - "leveraging side observations in stochastic bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"

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.IR(Information Retrieval)