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: "geometry of online packing linear programs"
  Abstract: We consider packing LP's with $m$ rows where all constraint coefficients are normalized to be in the unit interval. The n columns arrive in random order and the goal is to set the corresponding decision variables irrevocably when they arrive so as to obtain a feasible solution maximizing the expected reward. Previous (1 - \epsilon)-competitive algorithms require the right-hand side of the LP to be Omega((m/\epsilon^2) log (n/\epsilon)), a bound that worsens with the number of columns and rows. However, the dependence on the number of columns is not required in the single-row case and known lower bounds for the general case are also independent of n. #R##N#Our goal is to understand whether the dependence on n is required in the multi-row case, making it fundamentally harder than the single-row version. We refute this by exhibiting an algorithm which is (1 - \epsilon)-competitive as long as the right-hand sides are Omega((m^2/\epsilon^2) log (m/\epsilon)). Our techniques refine previous PAC-learning based approaches which interpret the online decisions as linear classifications of the columns based on sampled dual prices. The key ingredient of our improvement comes from a non-standard covering argument together with the realization that only when the columns of the LP belong to few 1-d subspaces we can obtain small such covers; bounding the size of the cover constructed also relies on the geometry of linear classifiers. General packing LP's are handled by perturbing the input columns, which can be seen as making the learning problem more robust.

Connected through hyperedge HE-1 (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"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-2 (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-3 (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"
    - "composable and efficient mechanisms"
    - "sequential auctions and externalities"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic matching online actions based on offline statistics"

Connected through hyperedge HE-4 (induced by source paper "online submodular maximization problem with vector packing constraint"):
  with members:
    - "online submodular maximization with preemption"
    - "online bipartite matching with decomposable weights"
    - "improved bounds for online preemptive matching"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "online stochastic packing applied to display ad allocation"

Connected through hyperedge HE-5 (induced by source paper "how the experts algorithm can help solve lps online"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "bandits with concave rewards and convex knapsacks"
    - "fast algorithms for online stochastic convex programming"
    - "sublinear optimization for machine learning"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "approximate convex optimization by online game playing"
    - "sequential and parallel algorithms for mixed packing and covering"

Connected through hyperedge HE-6 (induced by source paper "bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "linear contextual bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "adaptive crowdsourcing algorithms for the bandit survey problem"
    - "knapsack based optimal policies for budget limited multi armed bandits"

Connected through hyperedge HE-7 (induced by source paper "online independent set beyond the worst case secretaries prophets and periods"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "universally truthful secondary spectrum auctions"
    - "primal beats dual on online packing lps in the random order model"
    - "matroid prophet inequalities"
    - "the power of non uniform wireless power"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "wireless capacity with oblivious power in general metrics"
    - "a constant factor approximation for wireless capacity maximization with power control in the sinr model"

Connected through hyperedge HE-8 (induced by source paper "primal beats dual on online packing lps in the random order model"):
  with members:
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "algorithms for secretary problems on graphs and hypergraphs"

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)