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: "primal beats dual on online packing lps in the random order model"
  Abstract: We study packing LPs in an online model where the columns are presented to the algorithm in random order. This natural problem was investigated in various recent studies motivated, e.g., by online ad allocations and yield management where rows correspond to resources and columns to requests specifying demands for resources. Our main contribution is a $1-O(\sqrt{(\log{d})/B})$-competitive online algorithm, where $d$ denotes the column sparsity, i.e., the maximum number of resources that occur in a single column, and $B$ denotes the capacity ratio $B$, i.e., the ratio between the capacity of a resource and the maximum demand for this resource. In other words, we achieve a $(1 - \epsilon)$-approximation if the capacity ratio satisfies $B=\Omega((\log d)/\epsilon^2)$, which is known to be best-possible for any (randomized) online algorithms. #R##N#Our result improves exponentially on previous work with respect to the capacity ratio. In contrast to existing results on packing LP problems, our algorithm does not use dual prices to guide the allocation of resources. Instead, it simply solves, for each request, a scaled version of the partially known primal program and randomly rounds the obtained fractional solution to obtain an integral allocation for this request. We show that this simple algorithmic technique is not restricted to packing LPs with large capacity ratio: We prove an upper bound on the competitive ratio of $\Omega(d^{-1/(B-1)})$, for any $B \ge 2$. In addition, we show that our approach can be combined with VCG payments and obtain an incentive compatible $(1-\epsilon)$-competitive mechanism for packing LPs with $B=\Omega((\log m)/\epsilon^2)$, where $m$ is the number of constraints. Finally, we apply our technique to the generalized assignment problem for which we obtain the first online algorithm with competitive ratio $O(1)$.

Connected through hyperedge HE-1 (induced by source paper "random order models"):
  with members:
    - "robust algorithms for the secretary problem"
    - "prophet secretary for combinatorial auctions and matroids"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "secretary problems with non uniform arrival order"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "prophet inequalities with limited information"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-2 (induced by source paper "an optimal algorithm for online multiple knapsack"):
  with members:
    - "online knapsack problems with a resource buffer"
    - "online knapsack problem and budgeted truthful bipartite matching"

Connected through hyperedge HE-3 (induced by source paper "the bayesian prophet a low regret framework for online decision making"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "online stochastic matching new algorithms and bounds"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "online convex optimization using predictions"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "fast algorithms for online stochastic convex programming"
    - "bandits with knapsacks"

Connected through hyperedge HE-4 (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"
    - "online submodular welfare maximization greedy is optimal"
    - "selective call out and real time bidding"
    - "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"
    - "randomized rounding without solving the linear program"

Connected through hyperedge HE-5 (induced by source paper "online multidimensional packing problems in the random order model"):
  with members:
    - "geometry of online packing linear programs"
    - "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-6 (induced by source paper "strong algorithms for the ordinal matroid secretary problem"):
  with members:
    - "combinatorial secretary problems with ordinal information"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "secretary problems with non uniform arrival order"
    - "the submodular secretary problem goes linear"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem"
    - "prophet inequalities with limited information"

Connected through hyperedge HE-7 (induced by source paper "bernoulli factories and black box reductions in mechanism design"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "non optimal mechanism design"
    - "fast algorithms for online stochastic convex programming"
    - "a near optimal dynamic learning algorithm for online matching problems with concave returns"
    - "bandits with knapsacks"
    - "multi parameter mechanisms with implicit payment computation"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "limitations of randomized mechanisms for combinatorial auctions"

Connected through hyperedge HE-8 (induced by source paper "stability of service under time of use pricing"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "truthful online scheduling with commitments"
    - "fast algorithms for online stochastic convex programming"
    - "combinatorial auctions via posted prices"
    - "sequential posted pricing and multi parameter mechanism design"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"

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)