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 stochastic packing applied to display ad allocation"
  Abstract: Inspired by online ad allocation, we study online stochastic packing linear programs from theoretical and practical standpoints. We first present a near-optimal online algorithm for a general class of packing linear programs which model various online resource allocation problems including online variants of routing, ad allocations, generalized assignment, and combinatorial auctions. As our main theoretical result, we prove that a simple primal-dual training-based algorithm achieves a (1 - o(1))-approximation guarantee in the random order stochastic model. This is a significant improvement over logarithmic or constant-factor approximations for the adversarial variants of the same problems (e.g. factor 1 - 1/e for online ad allocation, and \log m for online routing). We then focus on the online display ad allocation problem and study the efficiency and fairness of various training-based and online allocation algorithms on data sets collected from real-life display ad allocation system. Our experimental evaluation confirms the effectiveness of training-based primal-dual algorithms on real data sets, and also indicate an intrinsic trade-off between fairness and efficiency.

Connected through hyperedge HE-1 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems"):
  with members:
    - "fast algorithms for online stochastic convex programming"
    - "primal beats dual on online packing lps in the random order model"
    - "online submodular welfare maximization greedy is optimal"
    - "selective call out and real time bidding"
    - "online stochastic matching online actions based on offline statistics"
    - "a dynamic near optimal algorithm for online linear programming"
    - "online stochastic matching beating 1 1 e"
    - "randomized rounding without solving the linear program"

Connected through hyperedge HE-2 (induced by source paper "online multidimensional packing problems in the random order model"):
  with members:
    - "primal beats dual on online packing lps in the random order model"
    - "geometry of online packing linear programs"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "a dynamic near optimal algorithm for online linear programming"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-3 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

Connected through hyperedge HE-4 (induced by source paper "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "improved approximation algorithms for stochastic matching"
    - "fast algorithms for online stochastic convex programming"
    - "a stochastic probing problem with applications"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "online stochastic matching online actions based on offline statistics"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-5 (induced by source paper "submodular secretary problem with shortlists"):
  with members:
    - "online submodular maximization beating 1 2 made simple"
    - "the submodular secretary problem under a cardinality constraint and with limited resources"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "the submodular secretary problem goes linear"
    - "lazier than lazy greedy"
    - "submodular maximization meets streaming matchings matroids and more"
    - "online submodular welfare maximization greedy is optimal"

Connected through hyperedge HE-6 (induced by source paper "the second price knapsack problem near optimal real time bidding in internet advertisement"):
  with members:
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "multiplicative pacing equilibria in auction markets"
    - "fast algorithms for online stochastic convex programming"
    - "real time bidding benchmarking with ipinyou dataset"
    - "bandits with knapsacks"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-7 (induced by source paper "online submodular welfare maximization greedy beats 1 2 in random order"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "randomized composable core sets for distributed submodular maximization"
    - "bayesian sequential auctions"
    - "online submodular welfare maximization greedy is optimal"
    - "geometry of online packing linear programs"
    - "composable and efficient mechanisms"
    - "sequential auctions and externalities"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-8 (induced by source paper "online weighted matching beating the frac 1 2 barrier"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "online submodular welfare maximization greedy is optimal"
    - "online stochastic matching beating 1 1 e"
    - "combinatorial auctions with decreasing marginal utilities"

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)