human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "beyond bidimensionality parameterized subexponential algorithms on directed graphs"):
  Abstract: We develop two different methods to achieve subexponential time parameterized algorithms for problems on sparse directed graphs. We exemplify our approaches with two well studied problems. #R##N#For the first problem, {\sc $k$-Leaf Out-Branching}, which is to find an oriented spanning tree with at least $k$ leaves, we obtain an algorithm solving the problem in time $2^{O(\sqrt{k} \log k)} n+ n^{O(1)}$ on directed graphs whose underlying undirected graph excludes some fixed graph $H$ as a minor. For the special case when the input directed graph is planar, the running time can be improved to $2^{O(\sqrt{k})}n + n^{O(1)}$. The second example is a generalization of the {\sc Directed Hamiltonian Path} problem, namely {\sc $k$-Internal Out-Branching}, which is to find an oriented spanning tree with at least $k$ internal vertices. We obtain an algorithm solving the problem in time $2^{O(\sqrt{k} \log k)} + n^{O(1)}$ on directed graphs whose underlying undirected graph excludes some fixed apex graph $H$ as a minor. Finally, we observe that for any $\epsilon>0$, the {\sc $k$-Directed Path} problem is solvable in time $O((1+\epsilon)^k n^{f(\epsilon)})$, where $f$ is some function of $\ve$. #R##N#Our methods are based on non-trivial combinations of obstruction theorems for undirected graphs, kernelization, problem specific combinatorial structures and a layering technique similar to the one employed by Baker to obtain PTAS for planar graphs.

Includes members:
  Member "efficient computation of representative sets with applications in parameterized and exact algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extensor coding")
  Member "subexponential parameterized algorithm for computing the cutwidth of a semi complete digraph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parameterizing edge modification problems above lower bounds")
  Member "solving connectivity problems parameterized by treewidth in single exponential time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hamiltonian cycle parameterized by treedepth in single exponential time and polynomial space")
  Member "faster algorithms for feedback arc set tournament kemeny rank aggregation and betweenness tournament":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameterizing edge modification problems above lower bounds")
  Member "on finding directed trees with many leaves":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy risks of explaining machine learning models")
  Member "faster fast feedback arc set in tournaments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parameterizing edge modification problems above lower bounds")
  Member "algorithm for finding k vertex out trees and its application to k internal out branching problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "designing deterministic polynomial space algorithms by color coding multivariate polynomials")
  Member "a linear vertex kernel for maximum internal spanning tree":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reoptimization of parameterized problems")

Question: Please tell me which class the source paper 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)