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: "linear recognition of almost interval graphs"
  Abstract: Let $\mbox{interval} + k v$, $\mbox{interval} + k e$, and $\mbox{interval} - k e$ denote the classes of graphs that can be obtained from some interval graph by adding $k$ vertices, adding $k$ edges, and deleting $k$ edges, respectively. When $k$ is small, these graph classes are called almost interval graphs. They are well motivated from computational biology, where the data ought to be represented by an interval graph while we can only expect an almost interval graph for the best. For any fixed $k$, we give linear-time algorithms for recognizing all these classes, and in the case of membership, our algorithms provide also a specific interval graph as evidence. When $k$ is part of the input, these problems are also known as graph modification problems, all NP-complete. Our results imply that they are fixed-parameter tractable parameterized by $k$, thereby resolving the long-standing open problem on the parameterized complexity of recognizing $\mbox{interval}+ k e$, first asked by Bodlaender et al. [Bioinformatics, 11:49--57, 1995]. Moreover, our algorithms for recognizing $\mbox{interval}+ k v$ and $\mbox{interval}- k e$ run in times $O(6^k \cdot (n + m))$ and $O(8^k \cdot (n + m))$, (where $n$ and $m$ stand for the numbers of vertices and edges respectively in the input graph,) significantly improving the $O(k^{2k}\cdot n^3m)$-time algorithm of Heggernes et al. [STOC 2007] and the $O(10^k \cdot n^9)$-time algorithm of Cao and Marx [SODA 2014] respectively.

Connected through hyperedge HE-1 (induced by source paper "subexponential parameterized algorithms and kernelization on almost chordal graphs"):
  with members:
    - "a complexity dichotomy for hitting connected minors on bounded treewidth graphs the chair and the banner draw the boundary"
    - "feedback vertex set inspired kernel for chordal vertex deletion"
    - "approximation and kernelization for chordal vertex deletion"
    - "polynomial kernels for weighted problems"
    - "large induced subgraphs via triangulations and cmso"
    - "data reduction for graph coloring problems"
    - "interval deletion is fixed parameter tractable"
    - "kernelization lower bounds by cross composition"

Connected through hyperedge HE-2 (induced by source paper "slightly superexponential parameterized problems"):
  with members:
    - "parameterized vertex deletion problems for hereditary graph classes with a block property"
    - "on the computational complexity of vertex integrity and component order connectivity"
    - "solving connectivity problems parameterized by treewidth in single exponential time"
    - "problems parameterized by treewidth tractable in single exponential time a logical approach"
    - "bandwidth and distortion revisited"
    - "planar subgraph isomorphism revisited"
    - "randomized algorithms for the loop cutset problem"
    - "on the closest string and substring problems"

Connected through hyperedge HE-3 (induced by source paper "feedback vertex set inspired kernel for chordal vertex deletion"):
  with members:
    - "polylogarithmic approximation algorithms for weighted mathcal f deletion problems"
    - "turing kernelization for finding long paths and cycles in restricted graph classes"
    - "approximation and kernelization for chordal vertex deletion"
    - "bidimensionality and kernels"
    - "unit interval vertex deletion fewer vertices are relevant"
    - "uniform kernelization complexity of hitting forbidden minors"
    - "chordal editing is fixed parameter tractable"
    - "network sparsification for steiner problems on planar and bounded genus graphs"

Connected through hyperedge HE-4 (induced by source paper "vertex deletion problems on chordal graphs"):
  with members:
    - "approximation and kernelization for chordal vertex deletion"
    - "faster online matrix vector multiplication"
    - "exact algorithms via monotone local search"
    - "approximate association via dissociation"
    - "unit interval editing is fixed parameter tractable"
    - "chordal editing is fixed parameter tractable"
    - "large induced subgraphs via triangulations and cmso"
    - "largest chordal and interval subgraphs faster than 2 n"

Connected through hyperedge HE-5 (induced by source paper "a linear time parameterized algorithm for directed feedback vertex set"):
  with members:
    - "finding small patterns in permutations in linear time"
    - "half integrality lp branching and fpt algorithms"
    - "linear time fpt algorithms via network flow"
    - "faster deterministic feedback vertex set"
    - "faster parameterized algorithms using linear programming"
    - "finding small separators in linear time via treewidth reduction"
    - "solving connectivity problems parameterized by treewidth in single exponential time"
    - "multicut is fpt"

Connected through hyperedge HE-6 (induced by source paper "approximation and kernelization for chordal vertex deletion"):
  with members:
    - "uniform kernelization complexity of hitting forbidden minors"
    - "a subexponential parameterized algorithm for proper interval completion"
    - "kernelization using structural parameters on sparse graph classes"
    - "a polynomial kernel for proper interval vertex deletion"
    - "preprocessing subgraph and minor problems when does a small vertex cover help"
    - "approximation and tidying a problem kernel for s plex cluster vertex deletion"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"

Connected through hyperedge HE-7 (induced by source paper "unit interval vertex deletion fewer vertices are relevant"):
  with members:
    - "approximation and kernelization for chordal vertex deletion"
    - "unit interval editing is fixed parameter tractable"
    - "a polynomial kernel for trivially perfect editing"

Connected through hyperedge HE-8 (induced by source paper "exact algorithms via monotone local search"):
  with members:
    - "exact algorithms for maximum independent set"
    - "faster algorithms to enumerate hypergraph transversals"
    - "a faster fpt algorithm and a smaller kernel for block graph vertex deletion"
    - "faster exact and parameterized algorithm for feedback vertex set in tournaments"
    - "on feedback vertex set new measure and new structures"
    - "an fpt algorithm and a polynomial kernel for linear rankwidth 1 vertex deletion"
    - "unit interval editing is fixed parameter tractable"
    - "a multivariate framework for weighted fpt algorithms"

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)