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: "budget optimization in search based advertising auctions"
  Abstract: Internet search companies sell advertisement slots based on users' search queries via an auction. While there has been a lot of attention on the auction process and its game-theoretic aspects, our focus is on the advertisers. In particular, the advertisers have to solve a complex optimization problem of how to place bids on the keywords of their interest so that they can maximize their return (the number of user clicks on their ads) for a given budget. We model the entire process and study this budget optimization problem. While most variants are NP hard, we show, perhaps surprisingly, that simply randomizing between two uniform strategies that bid equally on all the keywords works well. More precisely, this strategy gets at least 1-1/e fraction of the maximum clicks possible. Such uniform strategies are likely to be practical. We also present inapproximability results, and optimal algorithms for variants of the budget optimization problem.

Connected through hyperedge HE-1 (induced by source paper "personalized attraction enhanced sponsored search with multi task learning"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "learning multiple tasks with deep relationship networks"
    - "modeling attractiveness and multiple clicks in sponsored search results"
    - "truthful learning mechanisms for multi slot sponsored search auctions with externalities"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for sponsored search real time bidding"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "ladder a human level bidding agent for large scale real time online auctions"
    - "continuous deep q learning with model based acceleration"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "real time bidding for online advertising measurement and analysis"
    - "playing atari with deep reinforcement learning"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "budget optimization for sponsored search censored learning in mdps"

Connected through hyperedge HE-3 (induced by source paper "pacing equilibrium in first price auction markets"):
  with members:
    - "computing large market equilibria using abstractions"
    - "fair division without disparate impact"
    - "multiplicative pacing equilibria in auction markets"
    - "convex program duality fisher markets and nash social welfare"
    - "on revenue maximization in second price ad auctions"

Connected through hyperedge HE-4 (induced by source paper "a new optimization layer for real time bidding advertising campaigns"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"
    - "adam a method for stochastic optimization"
    - "sequential click prediction for sponsored search with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "multiplicative pacing equilibria in auction markets"):
  with members:
    - "budget optimization for sponsored search censored learning in mdps"
    - "online stochastic packing applied to display ad allocation"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-6 (induced by source paper "optimal bidding in repeated wireless spectrum auctions with budget constraints"):
  with members:
    - "budget optimization for sponsored search censored learning in mdps"
    - "secondary spectrum auctions for symmetric and submodular bidders"
    - "competition with dynamic spectrum leasing"

Connected through hyperedge HE-7 (induced by source paper "generalized second price auction with probabilistic broad match"):
  with members:
    - "on the ratio of revenue to welfare in single parameter mechanism design"
    - "send mixed signals earn more work less"
    - "hybrid keyword search auctions"
    - "algorithmic pricing via virtual valuations"

Connected through hyperedge HE-8 (induced by source paper "multiplicative bidding in online advertising"):
  with members:
    - "polyhedral clinching auctions and the adwords polytope"
    - "stochastic models for budget optimization in search based advertising"

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)