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: "the parameterized hardness of the k center problem in transportation networks"
  Abstract: In this paper we study the hardness of the $k$-Center problem on inputs that model transportation networks. For the problem, an edge-weighted graph $G=(V,E)$ and an integer $k$ are given and a center set $C\subseteq V$ needs to be chosen such that $|C|\leq k$. The aim is to minimize the maximum distance of any vertex in the graph to the closest center. This problem arises in many applications of logistics, and thus it is natural to consider inputs that model transportation networks. Such inputs are often assumed to be planar graphs, low doubling metrics, or bounded highway dimension graphs. For each of these models, parameterized approximation algorithms have been shown to exist. We complement these results by proving that the $k$-Center problem is W[1]-hard on planar graphs of constant doubling dimension, where the parameter is the combination of the number of centers $k$, the highway dimension $h$, and even the treewidth $t$. Moreover, under the Exponential Time Hypothesis there is no $f(k,t,h)\cdot n^{o(t+\sqrt{k+h})}$ time algorithm for any computable function $f$. Thus it is unlikely that the optimum solution to $k$-Center can be found efficiently, even when assuming that the input graph abides to all of the above models for transportation networks at once! #R##N#Additionally we give a simple parameterized $(1+\varepsilon)$-approximation algorithm for inputs of doubling dimension $d$ with runtime $(k/\varepsilon)^{O(kd)}\cdot n^{O(1)}$. This generalizes a previous result, which considered inputs in $d$-dimensional $L_q$ metrics.

Connected through hyperedge HE-1 (induced by source paper "grundy coloring friends half graphs bicliques"):
  with members:
    - "a characterization of b chromatic and partial grundy numbers by induced subgraphs"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "complexity of grundy coloring and its variants"
    - "deciding first order properties of nowhere dense graphs"
    - "designing fpt algorithms for cut problems using randomized contractions"

Connected through hyperedge HE-2 (induced by source paper "travelling on graphs with small highway dimension"):
  with members:
    - "hierarchy of transportation network parameters and hardness results"
    - "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"
    - "structural parameters tight bounds and approximation for k r center"
    - "beyond highway dimension small distance labels using tree skeletons"
    - "solving generalized maximum weight connected subgraph problem for network enrichment analysis"
    - "fixed parameter approximations for k center problems in low highway dimension graphs"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "efficient activity detection in untrimmed video with max subgraph search"

Connected through hyperedge HE-3 (induced by source paper "hierarchy of transportation network parameters and hardness results"):
  with members:
    - "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"
    - "beyond highway dimension small distance labels using tree skeletons"

Connected through hyperedge HE-4 (induced by source paper "near linear time approximation schemes for clustering in doubling metrics"):
  with members:
    - "travelling on graphs with small highway dimension"
    - "approximation schemes for capacitated clustering in doubling metrics"
    - "a fast approximation scheme for low dimensional k means"
    - "approximation schemes for clustering with outliers"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"

Connected through hyperedge HE-5 (induced by source paper "fixed parameter approximations for k center problems in low highway dimension graphs"):
  with members:
    - "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs"
    - "structural parameters tight bounds and approximation for k r center"
    - "polynomial time approximation schemes for k center and bounded capacity vehicle routing in metrics with bounded highway dimension"
    - "beyond highway dimension small distance labels using tree skeletons"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "parameterized approximation schemes using graph widths"

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.CC(Computational Complexity)