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 lower bounds via duality"
  Abstract: In this paper, we exploit linear programming duality in the online setting (i.e., where input arrives on the fly) from the unique perspective of designing lower bounds on the competitive ratio. In particular, we provide a general technique for obtaining online deterministic and randomized lower bounds (i.e., hardness results) on the competitive ratio for a wide variety of problems. We show the usefulness of our approach by providing new, tight lower bounds for three diverse online problems. The three problems we show tight lower bounds for are the Vector Bin Packing problem, Ad-auctions (and various online matching problems), and the Capital Investment problem. Our methods are sufficiently general that they can also be used to reconstruct existing lower bounds. Our techniques are in stark contrast to previous works, which exploit linear programming duality to obtain positive results, often via the useful primal-dual scheme. We design a general recipe with the opposite aim of obtaining negative results via duality. The general idea behind our approach is to construct a primal linear program based on a collection of input sequences, where the objective function corresponds to optimizing the competitive ratio. We then obtain the corresponding dual linear program and provide a feasible solution, where the objective function yields a lower bound on the competitive ratio. Online lower bounds are often achieved by adapting the input sequence according to an online algorithm’s behavior and doing an appropriate ad hoc case analysis. Using our unifying techniques, we simultaneously combine these cases into one linear program and achieve online lower bounds via a more robust analysis. We are confident that our framework can be successfully applied to produce many more lower bounds for a wide array of online problems.

Connected through hyperedge HE-1 (induced by source paper "tight bounds for online edge coloring"):
  with members:
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "an economic based analysis of ranking for online bipartite matching"
    - "tighter bounds for online bipartite matching"
    - "deterministic distributed edge coloring with fewer colors"
    - "dynamic algorithms for graph coloring"
    - "deterministic distributed edge coloring via hypergraph maximal matching"

Connected through hyperedge HE-2 (induced by source paper "online matching with general arrivals"):
  with members:
    - "tight bounds for online edge coloring"
    - "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"
    - "an economic based analysis of ranking for online bipartite matching"
    - "tighter bounds for online bipartite matching"
    - "maximum matching on trees in the online preemptive and the incremental dynamic graph models"
    - "online matroid intersection beating half for random arrival"

Connected through hyperedge HE-3 (induced by source paper "online unit clustering in higher dimensions"):
  with members:
    - "online unit covering in euclidean space"
    - "an improved lower bound for one dimensional online unit clustering"
    - "approximating sparse covering integer programs online"

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)