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: "solving problems on graphs of high rank width"
  Abstract: A modulator of a graph G to a specified graph class H is a set of vertices whose deletion puts G into H. The cardinality of a modulator to various tractable graph classes has long been used as a structural parameter which can be exploited to obtain FPT algorithms for a range of hard problems. Here we investigate what happens when a graph contains a modulator which is large but "well-structured" (in the sense of having bounded rank-width). Can such modulators still be exploited to obtain efficient algorithms? And is it even possible to find such modulators efficiently? #R##N#We first show that the parameters derived from such well-structured modulators are strictly more general than the cardinality of modulators and rank-width itself. Then, we develop an FPT algorithm for finding such well-structured modulators to any graph class which can be characterized by a finite set of forbidden induced subgraphs. We proceed by showing how well-structured modulators can be used to obtain efficient parameterized algorithms for Minimum Vertex Cover and Maximum Clique. Finally, we use well-structured modulators to develop an algorithmic meta-theorem for deciding problems expressible in Monadic Second Order (MSO) logic, and prove that this result is tight in the sense that it cannot be generalized to LinEMSO problems.

Connected through hyperedge HE-1 (induced by source paper "backdoors into heterogeneous classes of sat and csp"):
  with members:
    - "combining treewidth and backdoors for csp"
    - "discovering archipelagos of tractability for constraint satisfaction and counting"
    - "meta kernelization using well structured modulators"
    - "on backdoors to tractable constraint languages"
    - "backdoors to abduction"
    - "strong backdoors to bounded treewidth sat"
    - "the tractability of csp classes defined by forbidden patterns"
    - "backdoors to satisfaction"

Connected through hyperedge HE-2 (induced by source paper "a single exponential fixed parameter algorithm for distance hereditary vertex deletion"):
  with members:
    - "the behavior of clique width under graph operations and graph transformations"
    - "a polynomial kernel for distance hereditary vertex deletion"
    - "a faster fpt algorithm and a smaller kernel for block graph vertex deletion"
    - "meta kernelization using well structured modulators"
    - "a polynomial kernel for block graph deletion"
    - "an fpt algorithm and a polynomial kernel for linear rankwidth 1 vertex deletion"
    - "kernelization using structural parameters on sparse graph classes"
    - "linear kernels and single exponential algorithms via protrusion decompositions"

Connected through hyperedge HE-3 (induced by source paper "meta kernelization using well structured modulators"):
  with members:
    - "bidimensionality and kernels"
    - "kernelization using structural parameters on sparse graph classes"
    - "meta kernelization with structural parameters"
    - "kernelization lower bounds by cross composition"
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "preprocessing subgraph and minor problems when does a small vertex cover help"
    - "kernel bounds for path and cycle problems"
    - "preprocessing for treewidth a combinatorial analysis through kernelization"

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.DS(Data Structures and Algorithms)