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: "losing treewidth by separating subsets"
  Abstract: We study the problem of deleting the smallest set $S$ of vertices (resp.\ edges) from a given graph $G$ such that the induced subgraph (resp.\ subgraph) $G \setminus S$ belongs to some class $\mathcal{H}$. #R##N#We consider the case where graphs in $\mathcal{H}$ have treewidth bounded by $t$, and give a general framework to obtain approximation algorithms for both vertex and edge-deletion settings from approximation algorithms for certain natural graph partitioning problems called $k$-Subset Vertex Separator and $k$-Subset Edge Separator, respectively. #R##N#For the vertex deletion setting, our framework combined with the current best result for $k$-Subset Vertex Separator, yields a significant improvement in the approximation ratios for basic problems such as $k$-Treewidth Vertex Deletion and Planar-$F$ Vertex Deletion. Our algorithms are simpler than previous works and give the first uniform approximation algorithms under the natural parameterization. #R##N#For the edge deletion setting, we give improved approximation algorithms for $k$-Subset Edge Separator combining ideas from LP relaxations and important separators. We present their applications in bounded-degree graphs, and also give an APX-hardness result for the edge deletion problems.

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"
    - "improved analysis of highest degree branching for feedback vertex set"
    - "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"
    - "improved analysis of highest degree branching for feedback vertex set"
    - "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 "detecting feedback vertex sets of size k in o star 2 7 k time"):
  with members:
    - "a naive algorithm for feedback vertex set"
    - "fine grained parameterized complexity analysis of graph coloring problems"
    - "on problems as hard as cnf sat"
    - "exact algorithms via monotone local search"
    - "on feedback vertex set new measure and new structures"
    - "powers of tensors and fast matrix multiplication"
    - "faster deterministic feedback vertex set"
    - "solving connectivity problems parameterized by treewidth in single exponential time"

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)