human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "polynomial kernels for lambda extendible properties parameterized above the poljak turz ik bound"
  Abstract: Poljak and Turzik (Discrete Mathematics 1986) introduced the notion of {\lambda}-extendible properties of graphs as a generalization of the property of being bipartite. They showed that for any 0 < {\lambda} < 1 and {\lambda}-extendible property {\Pi}, any connected graph G on n vertices and m edges contains a spanning subgraph H in {\Pi} with at least {\lambda}m + (1-{\lambda})(n-1)/2 edges. The property of being bipartite is {\lambda}-extendible for {\lambda} = 1/2, and so the Poljak-Turzik bound generalizes the well-known Edwards-Erdos bound for Max-Cut. Other examples of {\lambda}-extendible properties include: being an acyclic oriented graph, a balanced signed graph, or a q-colorable graph for some integer q. #R##N#Mnich et. al. (FSTTCS 2012) defined the closely related notion of strong {\lambda}-extendibility. They showed that the problem of finding a subgraph satisfying a given strongly {\lambda}-extendible property {\Pi} is fixed-parameter tractable (FPT) when parameterized above the Poljak-Turzik bound - does there exist a spanning subgraph H of a connected graph G such that H in {\Pi} and H has at least {\lambda}m + (1-{\lambda})(n-1)/2 + k edges? - subject to the condition that the problem is FPT on a certain simple class of graphs called almost-forests of cliques. #R##N#In this paper we settle the kernelization complexity of nearly all problems parameterized above Poljak-Turzik bounds, in the affirmative. We show that these problems admit quadratic kernels (cubic when {\lambda} = 1/2), without using the assumption that the problem is FPT on almost-forests of cliques. Thus our results not only remove the technical condition of being FPT on almost-forests of cliques from previous results, but also unify and extend previously known kernelization results in this direction. Our results add to the select list of generic kernelization results known in the literature.

Connected through hyperedge HE-1 (induced by source paper "going far from degeneracy"):
  with members:
    - "faster deterministic parameterized algorithm for k path"
    - "raising the bar for vertex cover fixed parameter tractability above a higher guarantee"
    - "a randomized algorithm for long directed cycle"
    - "faster parameterized algorithms using linear programming"
    - "narrow sieves for parameterized paths and packings"
    - "vertex cover problem parameterized above and below tight bounds"
    - "solving max r sat above a tight lower bound"

Question: Please tell me which class the center node 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.DM(Discrete Mathematics)