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: "sequential selection of correlated ads by pomdps"
  Abstract: Online advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions but also lower the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and ads is a fine start but cannot solve the problem completely because good matching does not necessarily lead to good payoff. Moreover, with the limited display impressions, we need to balance the need of selecting ads to learn true ad payoffs (exploration) with that of allocating ads to generate high immediate payoffs based on the current belief (exploitation). In this paper, we address the problem by employing Partially observable Markov decision processes (POMDPs) and discuss how to utilize the correlation of ads to improve the efficiency of the exploration and increase ad incomes in a long run. Our mathematical derivation shows that the belief states of correlated ads can be naturally updated using a formula similar to collaborative filtering. To test our model, a real world ad dataset from a major search engine is collected and categorized. Experimenting over the data, we provide an analyse of the effect of the underlying parameters, and demonstrate that our algorithms significantly outperform other strong baselines.

Connected through hyperedge HE-1 (induced by source paper "real time bidding campaigns optimization using attribute selection"):
  with members:
    - "managing risk of bidding in display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "neural feature embedding for user response prediction in real time bidding rtb"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "smart pacing for effective online ad campaign optimization"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"

Connected through hyperedge HE-2 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous"):
  with members:
    - "risk aware dynamic reserve prices of programmatic guarantee in display advertising"
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
    - "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model"
    - "real time bidding for online advertising measurement and analysis"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"

Connected through hyperedge HE-3 (induced by source paper "budget constrained bidding by model free reinforcement learning in display advertising"):
  with members:
    - "multiplicative pacing equilibria in auction markets"
    - "real time bidding by reinforcement learning in display advertising"
    - "optimal real time bidding frameworks discussion"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "budget optimization for sponsored search censored learning in mdps"

Connected through hyperedge HE-4 (induced by source paper "real time bidding by reinforcement learning in display advertising"):
  with members:
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "statistical arbitrage mining for display advertising"
    - "smart pacing for effective online ad campaign optimization"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "budget optimization for sponsored search censored learning in mdps"

Connected through hyperedge HE-5 (induced by source paper "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"):
  with members:
    - "real time bidding for online advertising measurement and analysis"
    - "multi keyword multi click advertisement option contracts for sponsored search"
    - "shale an efficient algorithm for allocation of guaranteed display advertising"
    - "bidding for representative allocations for display advertising"

Connected through hyperedge HE-6 (induced by source paper "a lattice framework for pricing display ad options with the stochastic volatility underlying model"):
  with members:
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
    - "real time bidding for online advertising measurement and analysis"
    - "multi keyword multi click advertisement option contracts for sponsored search"

Connected through hyperedge HE-7 (induced by source paper "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model"):
  with members:
    - "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising"
    - "real time bidding for online advertising measurement and analysis"
    - "multi keyword multi click advertisement option contracts for sponsored search"

Connected through hyperedge HE-8 (induced by source paper "adaptive keywords extraction with contextual bandits for advertising on parked domains"):
  with members:
    - "a contextual bandit approach to personalized news article recommendation"
    - "linearly parameterized bandits"
    - "learning algorithms for keyphrase extraction"
    - "kea practical automatic keyphrase extraction"

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)