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: "fault tolerant spanners for doubling metrics better and simpler"
  Abstract: In STOC'95 Arya et al. (1995) conjectured that for any constant dimensional $n$-point Euclidean space, a $(1+\eps)$-spanner with constant degree, hop-diameter $O(\log n)$ and weight $O(\log n) \cdot \omega(MST)$ can be built in $O(n \log n)$ time. Recently Elkin and Solomon (technical report, April 2012) proved this conjecture of Arya et al. in the affirmative. In fact, the proof of Elkin and Solomon is more general in two ways. First, it applies to arbitrary doubling metrics. Second, it provides a complete tradeoff between the three involved parameters that is tight (up to constant factors) in the entire range. #R##N#Subsequently, Chan et al. (technical report, July 2012) provided another proof for Arya et al.'s conjecture, which is simpler than the proof of Elkin and Solomon. Moreover, Chan et al. (2012) also showed that one can build a fault-tolerant (FT) spanner with similar properties. Specifically, they showed that there exists a $k$-FT $(1+\eps)$-spanner with degree $O(k^2)$, hop-diameter $O(\log n)$ and weight $O(k^3 \cdot \log n) \cdot \omega(MST)$. The running time of the construction of Chan et al. was not analyzed. #R##N#In this work we improve the results of Chan et al., using a simpler proof. Specifically, we present a simple proof which shows that a $k$-FT $(1+\eps)$-spanner with degree $O(k^2)$, hop-diameter $O(\log n)$ and weight $O(k^2 \cdot \log n) \cdot \omega(MST)$ can be built in $O(n \cdot (\log n + k^2))$ time. Similarly to the constructions of Elkin and Solomon and Chan et al., our construction applies to arbitrary doubling metrics. However, in contrast to the construction of Elkin and Solomon, our construction fails to provide a complete (and tight) tradeoff between the three involved parameters. The construction of Chan et al. has this drawback too. #R##N#For random point sets in $\mathbb R^d$, we "shave" a factor of $\log n$ from the weight bound.

Connected through hyperedge HE-1 (induced by source paper "the minimum edge compact spanner network design problem"):
  with members:
    - "graph measures and network robustness"
    - "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"
    - "robust geometric spanners"
    - "fault tolerant spanners better and simpler"

Connected through hyperedge HE-2 (induced by source paper "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"):
  with members:
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "sparse fault tolerant bfs trees"
    - "efficient construction of spanners in d dimensions"
    - "on resilient graph spanners"
    - "robust geometric spanners"
    - "optimal euclidean spanners really short thin and lanky"
    - "incubators vs zombies fault tolerant short thin and lanky spanners for doubling metrics"
    - "balancing degree diameter and weight in euclidean spanners"

Connected through hyperedge HE-3 (induced by source paper "optimal euclidean spanners really short thin and lanky"):
  with members:
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"
    - "fast constructions of light weight spanners for general graphs"
    - "incubators vs zombies fault tolerant short thin and lanky spanners for doubling metrics"
    - "balancing degree diameter and weight in euclidean spanners"
    - "efficient regression in metric spaces via approximate lipschitz extension"
    - "an optimal time construction of euclidean sparse spanners with tiny diameter"
    - "shallow low and light trees and tight lower bounds for euclidean spanners"

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)