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: "computing the k nearest neighbors for all vertices via dijkstra"
  Abstract: We are given a directed graph $G = (V,E)$ with $n$ vertices and $m$ edges, with positive weights on the edges, and a parameter $k >0$. We show how to compute, for every vertex $v \in V$, its $k$ nearest-neighbors. The algorithm runs in $O( k ( n \log n + m ) )$ time, and follows by a somewhat careful modification of Dijkstra's shortest path algorithm. #R##N#This result is probably folklore, but we were unable to find a reference to it -- thus, this note.

Connected through hyperedge HE-1 (induced by source paper "power weighted shortest paths for unsupervised learning"):
  with members:
    - "cur decompositions similarity matrices and subspace clustering"
    - "exploration of a graph based density sensitive metric"
    - "semi supervised learning with density based distances"
    - "shortest path distance in random k nearest neighbor graphs"

Connected through hyperedge HE-2 (induced by source paper "simple and fast rounding algorithms for directed and node weighted multiway cut"):
  with members:
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "multiway cut pairwise realizable distributions and descending thresholds"
    - "multicommodity flows and cuts in polymatroidal networks"
    - "approximation algorithms for submodular multiway partition"
    - "submodular cost allocation problem and applications"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"
    - "rounding algorithms for a geometric embedding of minimum multiway cut"
    - "randomized rounding without solving the linear program"

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)