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: "shortest path and distance queries on road networks towards bridging theory and practice"
  Abstract: Given two locations $s$ and $t$ in a road network, a distance query returns the minimum network distance from $s$ to $t$, while a shortest path query computes the actual route that achieves the minimum distance. These two types of queries find important applications in practice, and a plethora of solutions have been proposed in past few decades. The existing solutions, however, are optimized for either practical or asymptotic performance, but not both. In particular, the techniques with enhanced practical efficiency are mostly heuristic-based, and they offer unattractive worst-case guarantees in terms of space and time. On the other hand, the methods that are worst-case efficient often entail prohibitive preprocessing or space overheads, which render them inapplicable for the large road networks (with millions of nodes) commonly used in modern map applications. #R##N#This paper presents {\em Arterial Hierarchy (AH)}, an index structure that narrows the gap between theory and practice in answering shortest path and distance queries on road networks. On the theoretical side, we show that, under a realistic assumption, AH answers any distance query in $\tilde{O}(\log \r)$ time, where $\r = d_{max}/d_{min}$, and $d_{max}$ (resp.\ $d_{min}$) is the largest (resp.\ smallest) $L_\infty$ distance between any two nodes in the road network. In addition, any shortest path query can be answered in $\tilde{O}(k + \log \r)$ time, where $k$ is the number of nodes on the shortest path. On the practical side, we experimentally evaluate AH on a large set of real road networks with up to twenty million nodes, and we demonstrate that (i) AH outperforms the state of the art in terms of query time, and (ii) its space and pre-computation overheads are moderate.

Connected through hyperedge HE-1 (induced by source paper "approximate computation for big data analytics"):
  with members:
    - "graph pattern matching for dynamic team formation"
    - "one pass error bounded trajectory simplification"
    - "investigative simulation towards utilizing graph pattern matching for investigative search"
    - "graph based anomaly detection and description a survey"
    - "bounded quadrant system error bounded trajectory compression on the go"
    - "capturing topology in graph pattern matching"

Connected through hyperedge HE-2 (induced by source paper "an introduction to graph data management"):
  with members:
    - "g core a core for future graph query languages"
    - "foundations of modern query languages for graph databases"
    - "the gremlin graph traversal machine and language invited talk"
    - "a brief study of open source graph databases"
    - "constructions from dots and lines"

Connected through hyperedge HE-3 (induced by source paper "the optimal route and stops for a group of users in a road network"):
  with members:
    - "fast detour computation for ride sharing"

Connected through hyperedge HE-4 (induced by source paper "local gaussian processes for efficient fine grained traffic speed prediction"):
  with members:
    - "efficient optimization for sparse gaussian process regression"

Connected through hyperedge HE-5 (induced by source paper "topcom index for shortest distance query in directed graph"):
  with members:
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "efficient single source shortest path and distance queries on large graphs"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "is label an independent set based labeling scheme for point to point distance querying on large graphs"

Connected through hyperedge HE-6 (induced by source paper "route planning in transportation networks"):
  with members:
    - "trip based public transit routing"
    - "optimal hub labeling is np complete"
    - "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"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "distance oracles for time dependent networks"
    - "transit node routing reconsidered"

Connected through hyperedge HE-7 (induced by source paper "efficient single source shortest path and distance queries on large graphs"):
  with members:
    - "is label an independent set based labeling scheme for point to point distance querying on large graphs"
    - "fast approximation of centrality"

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)