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: "stochastic models for budget optimization in search based advertising"
  Abstract: Internet search companies sell advertisement slots based on users' search queries via an auction. Advertisers have to determine how to place bids on the keywords of their interest in order to maximize their return for a given budget: this is the budget optimization problem. The solution depends on the distribution of future queries. #R##N#In this paper, we formulate stochastic versions of the budget optimization problem based on natural probabilistic models of distribution over future queries, and address two questions that arise. #R##N#[Evaluation] Given a solution, can we evaluate the expected value of the objective function? #R##N#[Optimization] Can we find a solution that maximizes the objective function in expectation? #R##N#Our main results are approximation and complexity results for these two problems in our three stochastic models. In particular, our algorithmic results show that simple prefix strategies that bid on all cheap keywords up to some level are either optimal or good approximations for many cases; we show other cases to be NP-hard.

Connected through hyperedge HE-1 (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-2 (induced by source paper "multiplicative bidding in online advertising"):
  with members:
    - "polyhedral clinching auctions and the adwords polytope"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-3 (induced by source paper "stochastic budget optimization in internet advertising"):
  with members:
    - "partout a distributed engine for efficient rdf processing"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-4 (induced by source paper "bid optimization in broad match ad auctions"):
  with members:
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-5 (induced by source paper "algorithmic methods for sponsored search advertising"):
  with members:
    - "a truthful mechanism for offline ad slot scheduling"
    - "sponsored search auctions with markovian users"
    - "bidding to the top vcg and equilibria of position based auctions"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-6 (induced by source paper "sponsored search auctions with markovian users"):
  with members:
    - "a truthful mechanism for offline ad slot scheduling"
    - "algorithmic methods for sponsored search advertising"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-7 (induced by source paper "budget optimization in search based advertising auctions"):
  with members:
    - "bidding to the top vcg and equilibria of position based auctions"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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)