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 "a heuristic use of dynamic programming to upperbound treewidth"):
  Abstract: For a graph $G$, let $\Omega(G)$ denote the set of all potential maximal cliques of $G$. For each subset $\Omega$ of $\Omega(G)$, let $\tw(G, \Omega)$ denote the smallest $k$ such that there is a tree-decomposition of $G$ of width $k$ whose bags all belong to $\Omega$. Bouchitte and Todinca observed in 2001 that $\tw(G, \Omega(G))$ is exactly the treewidth of $G$ and developed a dynamic programming algorithm to compute it. Indeed, their algorithm can readily be applied to an arbitrary non-empty subset $\Omega$ of $\Omega(G)$ and computes $\tw(G, \Omega)$, or reports that it is undefined, in time $|\Omega||V(G)|^{O(1)}$. This efficient tool for computing $\tw(G, \Omega)$ allows us to conceive of an iterative improvement procedure for treewidth upper bounds which maintains, as the current solution, a set of potential maximal cliques rather than a tree-decomposition. #R##N#We design and implement an algorithm along this approach. Experiments show that our algorithm vastly outperforms previously implemented heuristic algorithms for treewidth.

Includes members:
  Member "positive instance driven dynamic programming for treewidth":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding all global minimum cuts in practice")
  Member "a complete anytime algorithm for treewidth":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph convolutional policy for solving tree decomposition via reinforcement learning heuristics")
  Member "treewidth computation and extremal combinatorics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clustering to given connectivities")

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)