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: "online advertisement optimization and stochastic networks"
  Abstract: In this paper, we propose a stochastic model to describe how search service providers charge client companies based on users' queries for the keywords related to these companies' ads by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint, and design an online algorithm which captures the stochastic properties of users' queries and click-through behaviors. We solve the optimization problem by making connections to scheduling problems in wireless networks, queueing theory and stochastic networks. Unlike prior models, we do not assume that the number of query arrivals is known. Due to the stochastic nature of the arrival process considered here, either temporary "free" service, i.e., service above the specified budget or under-utilization of the budget is unavoidable. We prove that our online algorithm can achieve a revenue that is within $O(\epsilon)$ of the optimal revenue while ensuring that the overdraft or underdraft is $O(1/\epsilon)$, where $\epsilon$ can be arbitrarily small. With a view towards practice, we can show that one can always operate strictly under the budget. In addition, we extend our results to a click-through rate maximization model, and also show how our algorithm can be modified to handle non-stationary query arrival processes and clients with short-term contracts. #R##N#Our algorithm allows us to quantify the effect of errors in click-through rate estimation on the achieved revenue. We also show that in the long run, an expected overdraft level of $\Omega(\log(1/\epsilon))$ is unavoidable (a universal lower bound) under any stationary ad assignment algorithm which achieves a long-term average revenue within $O(\epsilon)$ of the offline optimum.

Connected through hyperedge HE-1 (induced by source paper "throughput optimal routing in blockchain based payment systems"):
  with members:
    - "atomic cross chain swaps"
    - "high throughput cryptocurrency routing in payment channel networks"
    - "settling payments fast and private efficient decentralized routing for path based transactions"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "privacy preserving shortest path computation"

Connected through hyperedge HE-2 (induced by source paper "is deadline oblivious scheduling efficient for controlling real time traffic in cellular downlink systems"):
  with members:
    - "adaptive 360 vr video streaming divide and conquer"
    - "timely throughput of heterogeneous wireless networks fundamental limits and algorithms"
    - "optimal scheduling for fair resource allocation in ad hoc networks with elastic and inelastic traffic"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning when all actions are not always available"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "using large ensembles of control variates for variational inference"
    - "planning and learning with stochastic action sets"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "policy gradient methods for reinforcement learning with function approximation and action dependent baselines"

Connected through hyperedge HE-4 (induced by source paper "online allocation with traffic spikes mixing adversarial and stochastic models"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-5 (induced by source paper "online knapsack problem under expected capacity constraint"):
  with members:
    - "online knapsack problem and budgeted truthful bipartite matching"
    - "algorithms for secretary problems on graphs and hypergraphs"

Connected through hyperedge HE-6 (induced by source paper "online budgeted repeated matching"):
  with members:
    - "adaptive replication in distributed content delivery networks"
    - "serving content with unknown demand the high dimensional regime"
    - "near optimal online algorithms for dynamic resource allocation problems"
    - "online stochastic packing applied to display ad allocation"
    - "algorithms for secretary problems on graphs and hypergraphs"

Connected through hyperedge HE-7 (induced by source paper "work capacity of freelance markets fundamental limits and decentralized schemes"):
  with members:
    - "cognitive coordination of global service delivery"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-8 (induced by source paper "efficient advert assignment"):
  with members:
    - "econometrics for learning agents"
    - "optimising trade offs among stakeholders in ad auctions"
    - "a dynamic axiomatic approach to first price auctions"
    - "bayesian sequential auctions"
    - "simplicity expressiveness tradeoffs in mechanism design"
    - "truthful mechanisms with implicit payment computation"
    - "bidding to the top vcg and equilibria of position based auctions"

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)