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 "everything you always wanted to know about the parameterized complexity of subgraph isomorphism but were afraid to ask"):
  Abstract: Given two graphs $H$ and $G$, the Subgraph Isomorphism problem asks if $H$ is isomorphic to a subgraph of $G$. While NP-hard in general, algorithms exist for various parameterized versions of the problem: for example, the problem can be solved (1) in time $2^{O(|V(H)|)}\cdot n^{O(\tw(H))}$ using the color-coding technique of Alon, Yuster, and Zwick; (2) in time $f(|V(H)|,\tw(G))\cdot n$ using Courcelle's Theorem; (3) in time $f(|V(H)|,\genus(G))\cdot n$ using a result on first-order model checking by Frick and Grohe; or (4) in time $f(\maxdeg(H))\cdot n^{O(\tw(G)})$ for connected $H$ using the algorithm of Matou\v{s}ek and Thomas. Already this small sample of results shows that the way an algorithm can depend on the parameters is highly nontrivial and subtle. #R##N#We develop a framework involving 10 relevant parameters for each of $H$ and $G$ (such as treewidth, pathwidth, genus, maximum degree, number of vertices, number of components, etc.), and ask if an algorithm with running time \[ f_1(p_1,p_2,..., p_\ell)\cdot n^{f_2(p_{\ell+1},..., p_k)} \] exist, where each of $p_1,..., p_k$ is one of the 10 parameters depending only on $H$ or $G$. We show that {\em all} the questions arising in this framework are answered by a set of 11 maximal positive results (algorithms) and a set of 17 maximal negative results (hardness proofs); some of these results already appear in the literature, while others are new in this paper. #R##N#On the algorithmic side, our study reveals for example that an unexpected combination of bounded degree, genus, and feedback vertex set number of $G$ gives rise to a highly nontrivial algorithm for Subgraph Isomorphism. On the hardness side, we present W[1]-hardness proofs under extremely restricted conditions, such as when $H$ is a bounded-degree tree of constant pathwidth and $G$ is a planar graph of bounded pathwidth.

Includes members:
  Member "on feedback vertex set new measure and new structures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hitting long directed cycles is fixed parameter tractable")
  Member "kernels for connected dominating set on graphs with excluded topological subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a general kernelization technique for domination and independence problems in sparse classes")
  Member "parameterized complexity of directed steiner tree on sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tight bounds for planar strongly connected steiner subgraph with fixed number of terminals and extensions")
  Member "linear kernels on graphs excluding topological minors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tight kernel bounds for problems on graphs with small degeneracy")
  Member "structure theorem and isomorphism test for graphs with excluded topological subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypergraph isomorphism for groups with restricted composition factors")
  Member "finding topological subgraphs is fixed parameter tractable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hitting topological minors is fpt")
  Member "deciding first order properties of locally tree decomposable structures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hitting topological minors is fpt")

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)