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 "on subexponential parameterized algorithms for steiner tree and directed subset tsp on planar graphs"):
  Abstract: There are numerous examples of the so-called square root phenomenon in the field of parameterized algorithms: many of the most fundamental graph problems, parameterized by some natural parameter $k$, become significantly simpler when restricted to planar graphs and in particular the best possible running time is exponential in $O(\sqrt{k})$ instead of $O(k)$ (modulo standard complexity assumptions). We consider two classic optimization problems parameterized by the number of terminals. The Steiner Tree problem asks for a minimum-weight tree connecting a given set of terminals T in an edge-weighted graph. In the Subset Traveling Salesman problem we are asked to visit all the terminals $T$ by a minimum-weight closed walk. We investigate the parameterized complexity of these problems in planar graphs, where the number $k = |T|$ of terminals is regarded as the parameter. Our results are the following: (1) Subset TSP can be solved in time $2^{O(\sqrt{k} \log k)} \cdot n^{O(1)}$ even on edge-weighted directed planar graphs. (2) Assuming the Exponential-Time Hypothesis, Steiner Tree on undirected planar graphs cannot be solved in time $2^{o(k)} \cdot n^{O(1)}$, even in the unit-weight setting. (3) Steiner Tree can be solved in time $n^{O(\sqrt{k})} \cdot W$ on undirected planar graphs with maximum edge weight $W$. A direct corollary of the combination of our results for Steiner Tree is that this problem does not admit a parameter-preserving polynomial kernel on planar graphs unless ETH fails.

Includes members:
  Member "slightly superexponential parameterized problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the complexity of finding large odd induced subgraphs and odd colorings")
  Member "subexponential parameterized algorithms for planar and apex minor free graphs via low treewidth pattern covering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tight bounds for planar strongly connected steiner subgraph with fixed number of terminals and extensions")
  Member "on problems as hard as cnf sat":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "optimal parameterized algorithms for planar facility location problems using voronoi diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grundy coloring friends half graphs bicliques")
  Member "a polynomial time bicriteria approximation scheme for planar bisection":
    also connected through other hyperedges:
      (none)
  Member "the two edge connectivity survivable network design problem in planar graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a ptas for subset tsp in minor free graphs")
  Member "network sparsification for steiner problems on planar and bounded genus graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a relaxation of the directed disjoint paths problem a global congestion metric helps")
  Member "known algorithms for edge clique cover are probably optimal":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "potential maximal cliques parameterized by edge clique cover")

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)