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 the approximability of the maximum agreement subtree and maximum compatible tree problems"):
  Abstract: The aim of this paper is to give a complete picture of approximability for two tree consensus problems which are of particular interest in computational biology: Maximum Agreement SubTree (MAST) and Maximum Compatible Tree (MCT). Both problems take as input a label set and a collection of trees whose leaf sets are each bijectively labeled with the label set. Define the size of a tree as the number of its leaves. The well-known MAST problem consists of finding a maximum-sized tree that is topologically embedded in each input tree, under label-preserving embeddings. Its variant MCT is less stringent, as it allows the input trees to be arbitrarily refined. Our results are as follows. We show that MCT is NP-hard to approximate within bound n^1^-^@e on rooted trees, where n denotes the size of each input tree; the same approximation lower bound was already known for MAST [J. Jansson, Consensus algorithms for trees and strings, Ph. D. Thesis, Lund University, 2003]. Furthermore, we prove that MCT on two rooted trees is not approximable within bound 2^l^o^g^^^1^^^-^^^@e^n, unless all problems in NP are solvable in quasi-polynomial time; the same result was previously established for MAST on three rooted trees [J. Hein, T. Jiang, L. Wang, K. Zhang, On the complexity of comparing evolutionary trees, Discrete Applied Mathematics 71 (1-3) (1996) 153-169] (note that MAST on two trees is solvable in polynomial time [M.A. Steel, T.J. Warnow, Kaikoura tree theorems: Computing the maximum agreement subtree, Information Processing Letters 48 (2) (1993) 77-82]). Let CMAST, resp. CMCT, denote the complement version of MAST, resp. MCT: CMAST, resp. CMCT, consists of finding a tree that is a feasible solution of MAST, resp. MCT, and whose leaf label set excludes a smallest subset of the input labels. The approximation threshold for CMAST, resp. CMCT, on rooted trees is shown to be the same as the approximation threshold for CMAST, resp. CMCT, on unrooted trees; it was already known that both CMAST and CMCT are approximable within ratio three on rooted and unrooted trees [V. Berry, F. Nicolas, Maximum agreement and compatible supertrees, in: S.C. Sahinalp, S. Muthukrishnan, U. Dogrusoz (Eds.), Proceedings of the 15th Annual Symposium on Combinatorial Pattern Matching, CPM'04, in: LNCS, vol. 3109, Springer-Verlag, 2004, pp. 205-219; G. Ganapathy, T.J. Warnow, Approximating the complement of the maximum compatible subset of leaves of k trees, in: K. Jansen, S. Leonardi, V. V. Vazirani (Eds.), Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization, APPROX'02, in: LNCS, vol. 2462, Springer-Verlag, 2002, pp. 122-134]. The latter results are completed by showing that CMAST is APX-hard on three rooted trees and that CMCT is APX-hard on two rooted trees.

Includes members:
  Member "solving the maximum agreement subtree and the maximum compatible tree problems on many bounded degree trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an analysis of the geodesic distance and other comparative metrics for tree like structures")
  Member "an even faster and more unifying algorithm for comparing trees via unbalanced bipartite matchings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster algorithms for the maximum common subtree isomorphism problem")

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.CC(Computational Complexity)