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: "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
  Abstract: We present prior robust algorithms for a large class of resource allocation problems where requests arrive one-by-one (online), drawn independently from an unknown distribution at every step. We design a single algorithm that, for every possible underlying distribution, obtains a $1-\epsilon$ fraction of the profit obtained by an algorithm that knows the entire request sequence ahead of time. The factor $\epsilon$ approaches $0$ when no single request consumes/contributes a significant fraction of the global consumption/contribution by all requests together. We show that the tradeoff we obtain here that determines how fast $\epsilon$ approaches $0$, is near optimal: we give a nearly matching lower bound showing that the tradeoff cannot be improved much beyond what we obtain. #R##N#Going beyond the model of a static underlying distribution, we introduce the adversarial stochastic input model, where an adversary, possibly in an adaptive manner, controls the distributions from which the requests are drawn at each step. Placing no restriction on the adversary, we design an algorithm that obtains a $1-\epsilon$ fraction of the optimal profit obtainable w.r.t. the worst distribution in the adversarial sequence. #R##N#In the offline setting we give a fast algorithm to solve very large LPs with both packing and covering constraints. We give algorithms to approximately solve (within a factor of $1+\epsilon$) the mixed packing-covering problem with $O(\frac{\gamma m \log (n/\delta)}{\epsilon^2})$ oracle calls where the constraint matrix of this LP has dimension $n\times m$, the success probability of the algorithm is $1-\delta$, and $\gamma$ quantifies how significant a single request is when compared to the sum total of all requests. #R##N#We discuss implications of our results to several special cases including online combinatorial auctions, network routing and the adwords problem.

Connected through hyperedge HE-1 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "online stochastic matching with edge arrivals"
    - "optimal single choice prophet inequalities from samples"
    - "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint"
    - "prophet inequality for bipartite matching merits of being simple and non adaptive"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "prophets and secretaries with overbooking"

Connected through hyperedge HE-2 (induced by source paper "robust online composition routing and nf placement for nfv enabled services"):
  with members:
    - "a virtual network customization framework for multicast services in nfv enabled core networks"
    - "distributed service function chaining"
    - "service function chaining resource allocation a survey"
    - "online admission control and embedding of service chains"
    - "scalable steiner tree for multicast communications in software defined networking"
    - "competitive and deterministic embeddings of virtual networks"

Connected through hyperedge HE-3 (induced by source paper "online assortment with limited inventories and set dependent revenues"):
  with members:
    - "optimal search segmentation mechanisms for online platform markets"
    - "multi stage and multi customer assortment optimization with inventory constraints"
    - "how the experts algorithm can help solve lps online"
    - "bandits with knapsacks"

Connected through hyperedge HE-4 (induced by source paper "prophet inequality for bipartite matching merits of being simple and non adaptive"):
  with members:
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "maximizing efficiency in dynamic matching markets"
    - "makespan minimization via posted prices"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online contention resolution schemes"
    - "the invisible hand of dynamic market pricing"

Connected through hyperedge HE-5 (induced by source paper "robust algorithms for the secretary problem"):
  with members:
    - "stochastic bandits robust to adversarial corruptions"
    - "learning geometric concepts with nasty noise"
    - "learning from untrusted data"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online and random order load balancing simultaneously"

Connected through hyperedge HE-6 (induced by source paper "satiation in fisher markets and approximation of nash social welfare"):
  with members:
    - "a strongly polynomial algorithm for linear exchange markets"
    - "approximating the nash social welfare with budget additive valuations"
    - "finding fair and efficient allocations"
    - "convex program duality fisher markets and nash social welfare"
    - "nash social welfare for indivisible items under separable piecewise linear concave utilities"
    - "nash social welfare matrix permanent and stable polynomials"
    - "an improved combinatorial polynomial algorithm for the linear arrow debreu market"
    - "apx hardness of maximizing nash social welfare with indivisible items"

Connected through hyperedge HE-7 (induced by source paper "the bayesian prophet a low regret framework for online decision making"):
  with members:
    - "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"
    - "primal beats dual on online packing lps in the random order model"

Connected through hyperedge HE-8 (induced by source paper "online linear programming dual convergence new algorithms and regret bounds"):
  with members:
    - "introduction to online convex optimization"
    - "a primal dual learning algorithm for personalized dynamic pricing with an inventory constraint"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "fast algorithms for online stochastic convex programming"
    - "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)