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: "improved analysis of highest degree branching for feedback vertex set"
  Abstract: Recent empirical evaluations of exact algorithms for Feedback Vertex Set have demonstrated the efficiency of a highest-degree branching algorithm with a degree-based pruning heuristic. In this paper, we prove that this empirically fast algorithm runs in $O(3.460^k n)$ time, where $k$ is the solution size. This improves the previous best $O(3.619^k n)$-time deterministic algorithm obtained by Kociumaka and Pilipczuk.

Connected through hyperedge HE-1 (induced by source paper "hitting topological minors is fpt"):
  with members:
    - "hitting topological minor models in planar graphs is fixed parameter tractable"
    - "losing treewidth by separating subsets"
    - "a 2 approximation algorithm for feedback vertex set in tournaments"
    - "polylogarithmic approximation for minimum planarization almost"
    - "exact algorithms for maximum independent set"
    - "bidimensionality and kernels"
    - "exact algorithms via monotone local search"
    - "improved bounds for the flat wall theorem"

Connected through hyperedge HE-2 (induced by source paper "reducing topological minor containment to the unique linkage theorem"):
  with members:
    - "hitting topological minor models in planar graphs is fixed parameter tractable"
    - "losing treewidth by separating subsets"
    - "a 2 approximation algorithm for feedback vertex set in tournaments"
    - "polylogarithmic approximation for minimum planarization almost"
    - "exact algorithms for maximum independent set"
    - "linear kernels for edge deletion problems to immersion closed graph classes"
    - "bidimensionality and kernels"
    - "exact algorithms via monotone local search"

Connected through hyperedge HE-3 (induced by source paper "fpt algorithms for generalized feedback vertex set problems"):
  with members:
    - "detecting feedback vertex sets of size k in o star 2 7 k time"
    - "smaller parameters for vertex cover kernelization"
    - "on feedback vertex set new measure and new structures"
    - "directed subset feedback vertex set is fixed parameter tractable"
    - "subset feedback vertex set is fixed parameter tractable"

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)