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: "fine grained complexity analysis of two classic tsp variants"
  Abstract: We analyze two classic variants of the Traveling Salesman Problem using the toolkit of fine-grained complexity. Our first set of results is motivated by the Bitonic TSP problem: given a set of $n$ points in the plane, compute a shortest tour consisting of two monotone chains. It is a classic dynamic-programming exercise to solve this problem in $O(n^2)$ time. While the near-quadratic dependency of similar dynamic programs for Longest Common Subsequence and Discrete Frechet Distance has recently been proven to be essentially optimal under the Strong Exponential Time Hypothesis, we show that bitonic tours can be found in subquadratic time. More precisely, we present an algorithm that solves bitonic TSP in $O(n \log^2 n)$ time and its bottleneck version in $O(n \log^3 n)$ time. Our second set of results concerns the popular $k$-OPT heuristic for TSP in the graph setting. More precisely, we study the $k$-OPT decision problem, which asks whether a given tour can be improved by a $k$-OPT move that replaces $k$ edges in the tour by $k$ new edges. A simple algorithm solves $k$-OPT in $O(n^k)$ time for fixed $k$. For 2-OPT, this is easily seen to be optimal. For $k=3$ we prove that an algorithm with a runtime of the form $\tilde{O}(n^{3-\epsilon})$ exists if and only if All-Pairs Shortest Paths in weighted digraphs has such an algorithm. The results for $k=2,3$ may suggest that the actual time complexity of $k$-OPT is $\Theta(n^k)$. We show that this is not the case, by presenting an algorithm that finds the best $k$-move in $O(n^{\lfloor 2k/3 \rfloor + 1})$ time for fixed $k \geq 3$. This implies that 4-OPT can be solved in $O(n^3)$ time, matching the best-known algorithm for 3-OPT. Finally, we show how to beat the quadratic barrier for $k=2$ in two important settings, namely for points in the plane and when we want to solve 2-OPT repeatedly.

Connected through hyperedge HE-1 (induced by source paper "maintaining the union of unit discs under insertions with near optimal overhead"):
  with members:
    - "sensory regimes of effective distributed searching without leaders"
    - "dynamic planar voronoi diagrams for general distance functions and their algorithmic applications"

Connected through hyperedge HE-2 (induced by source paper "near optimal algorithms for shortest paths in weighted unit disk graphs"):
  with members:
    - "dynamic planar voronoi diagrams for general distance functions and their algorithmic applications"
    - "shortest paths in intersection graphs of unit disks"

Connected through hyperedge HE-3 (induced by source paper "fine grained complexity of k opt in bounded degree graphs for solving tsp"):
  with members:
    - "homomorphisms are a good basis for counting small subgraphs"
    - "popular conjectures imply strong lower bounds for dynamic problems"

Connected through hyperedge HE-4 (induced by source paper "on the fine grained complexity of one dimensional dynamic programming"):
  with members:
    - "fast exact k means k medians and bregman divergence clustering in 1d"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "clustered integer 3sum via additive combinatorics"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"
    - "a faster pseudopolynomial time algorithm for subset sum"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-5 (induced by source paper "on the complexity of restoring corrupted colorings"):
  with members:
    - "on the hardness of losing weight"
    - "fixing improper colorings of graphs"
    - "finding shortest paths between graph colourings"
    - "reconfiguration in bounded bandwidth and treedepth"
    - "kernelization lower bounds by cross composition"
    - "adaptive uncertainty resolution in bayesian combinatorial optimization problems"

Connected through hyperedge HE-6 (induced by source paper "improving tsp tours using dynamic programming over tree decomposition"):
  with members:
    - "finding induced subgraphs via minimal triangulations"

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)