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: "pinning down the strong wilber 1 bound for binary search trees"
  Abstract: The famous dynamic optimality conjecture of Sleator and Tarjan from 1985 conjectures the existence of an $O(1)$-competitive algorithm for binary search trees (BST's). Even the simpler problem of (offline) approximation of the optimal cost of a BST for a given input, that we denote by $OPT$, is still widely open, with the best current algorithm achieving an $O(\log\log n)$-approximation. A major challenge in designing such algorithms is to obtain a tight lower bound on $OPT$ that is algorithm friendly. Although several candidate lower bounds were suggested in the past, such as WB-1 and WB-2 bounds by Wilber, and Independent Rectangles bound by Demaine et al., the only currently known non-trivial approximation algorithm achieves an $O(\log\log n)$ approximation factor by comparing $OPT$ with a weak variant of WB-1, that uses a \emph{fixed} partitioning of the keys. This bound, however, is known to have a gap of $\Omega(\log\log n)$, and therefore it cannot yield better approximation algorithms. To overcome this obstacle, it is natural to consider a stronger variant of WB-1, that maximizes the bound over \emph{all} partitionings of the keys. An interesting question, mentioned by Iacono and by Kozma, is whether the $O(\log\log n)$-approximation can be improved by using this stronger bound. #R##N#In this paper, we show that the gap between the stronger WB-1 bound and $OPT$ may be as large as $\Omega(\log\log n/\log\log\log n)$. This rules out the hope of obtaining better approximation algorithms via the only known algorithmic approach, combined with the stronger WB-1 bound. We also provide algorithmic results: for any parameter $D$, we present a simple $O(D)$-approximation algorithm with running time $\exp\left(O\left (n^{1/2^D}\log n\right )\right )$. This implies an $O(1)$-approximation algorithm in sub-exponential time.

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)