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: "optimal hub labeling is np complete"
  Abstract: AbstractDistance labeling is a preprocessing technique introduced by Peleg [Jour-nal of Graph Theory, 33(3)] to speed up distance queries in large networks.Herein, each vertex receives a (short) label and, the distance between twovertices can be inferred from their two labels. One such preprocessingproblem occurs in the hub labeling algorithm [Abraham et al., SODA’10]:the label of a vertex v is a set of vertices x (the “hubs”) with their dis-tance d(x,v) to v and the distance between any two vertices u and v isthe sum of their distances to a common hub. The problem of assigning asfew such hubs as possible was conjectured to be NP-hard, but no proofwas known to date. We give a reduction from the well-known VertexCoverproblem on graphs to prove that ﬁnding an optimal hub labelingis indeed NP-hard. 1 Introduction Finding shortest paths quickly is an essential part of many real-world businesseslike maps services and programming of mobile GPS navigation devices. In theseapplications, Dijkstra’s algorithm proved much too slow, especially since typicalroad maps contain millions of vertices and edges. However, since road mapsundergo little changes, a preprocessing based approach seems reasonable. Inthis spirit, Peleg [12] suggested constructing a distributed data structure inadvance, allowing later distance queries to be answered in sublinear time. Thisdistributed data structure comprises a (short) label for each vertex such thatthe distance between two vertices can be inferred from their respective labels.Previous work on distance labeling has been focused on ﬁnding small labels inpolynomial time on a variety of graph classes [1, 5, 6, 8, 9, 11] or on generalgraphs [11, 13] (see also the survey of Gavoille and Peleg [10]).Abraham et al. [2, 3] described the following preprocessing: each vertex vreceives, as a label, a list of vertices x with their distance d(x,v) to v suchthat, for each two vertices u and v, there is a vertex (the “hub”) on a shortestu-v-path that is in both, the label of u and the label of v. Once all labelshave been computed, the distance between u and v is the minimum over all

Connected through hyperedge HE-1 (induced by source paper "route planning in transportation networks"):
  with members:
    - "trip based public transit routing"
    - "salt a unified framework for all shortest path query variants on road networks"
    - "two phase bicriterion search for finding fast and efficient electric vehicle routes"
    - "customizable contraction hierarchies"
    - "shortest path and distance queries on road networks towards bridging theory and practice"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "distance oracles for time dependent networks"
    - "transit node routing reconsidered"

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)