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: "partitioning a graph into small pieces with applications to path transversal"
  Abstract: Given a graph $G = (V, E)$ and an integer $k$, we study $k$-Vertex Seperator (resp. $k$-Edge Separator), where the goal is to remove the minimum number of vertices (resp. edges) such that each connected component in the resulting graph has at most $k$ vertices. Our primary focus is on the case where $k$ is either a constant or a slowly growing function of $n$ (e.g. $O(\log n)$ or $n^{o(1)}$). Our problems can be interpreted as a special case of three general classes of problems that have been studied separately (balanced graph partitioning, Hypergraph Vertex Cover (HVC), and fixed parameter tractability (FPT)). #R##N#Our main result is an $O(\log k)$-approximation algorithm for $k$-Vertex Seperator that runs in time $2^{O(k)} n^{O(1)}$, and an $O(\log k)$-approximation algorithm for $k$-Edge Separator that runs in time $n^{O(1)}$. Our result on $k$-Edge Seperator improves the best previous graph partitioning algorithm for small $k$. Our result on $k$-Vertex Seperator improves the simple $(k+1)$-approximation from HVC. When $OPT > k$, the running time $2^{O(k)} n^{O(1)}$ is faster than the lower bound $k^{\Omega(OPT)} n^{\Omega(1)}$ for exact algorithms assuming the Exponential Time Hypothesis. While the running time of $2^{O(k)} n^{O(1)}$ for $k$-Vertex Separator seems unsatisfactory, we show that the superpolynomial dependence on $k$ may be needed to achieve a polylogarithmic approximation ratio, based on hardness of Densest $k$-Subgraph. #R##N#We also study $k$-Path Transversal, where the goal is to remove the minimum number of vertices such that there is no simple path of length $k$. With additional ideas from FPT algorithms and graph theory, we present an $O(\log k)$-approximation algorithm for $k$-Path Transversal that runs in time $2^{O(k^3 \log k)} n^{O(1)}$. Previously, the existence of even $(1 - \delta)k$-approximation algorithm for fixed $\delta > 0$ was open.

Connected through hyperedge HE-1 (induced by source paper "parameterized complexity of safe set"):
  with members:
    - "linear kernels for separating a graph into components of bounded size"
    - "generalized feedback vertex set problems on bounded treewidth graphs chordality is the key to single exponential parameterized algorithms"
    - "solving integer linear programs with a small number of global variables and constraints"
    - "on the computational complexity of vertex integrity and component order connectivity"

Connected through hyperedge HE-2 (induced by source paper "losing treewidth by separating subsets"):
  with members:
    - "linear kernels for edge deletion problems to immersion closed graph classes"
    - "parameterized vertex deletion problems for hereditary graph classes with a block property"
    - "approximation and kernelization for chordal vertex deletion"
    - "a single exponential fixed parameter algorithm for distance hereditary vertex deletion"
    - "lp based robust algorithms for noisy minor free and bounded treewidth graphs"
    - "on the computational complexity of vertex integrity and component order connectivity"
    - "polynomial bounds for the grid minor theorem"
    - "fixed parameter tractability of multicut parameterized by the size of the cutset"

Connected through hyperedge HE-3 (induced by source paper "sherali adams integrality gaps matching the log density threshold"):
  with members:
    - "label optimal regret bounds for online local learning"

Connected through hyperedge HE-4 (induced by source paper "being corrupt requires being clever but detecting corruption doesn t"):
  with members:
    - "reductions between expansion problems"

Connected through hyperedge HE-5 (induced by source paper "almost polynomial ratio eth hardness of approximating densest k subgraph"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "eth hardness for signaling in symmetric zero sum games"
    - "approximating dense max 2 csps"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"

Connected through hyperedge HE-6 (induced by source paper "lp based robust algorithms for noisy minor free and bounded treewidth graphs"):
  with members:
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial 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)