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: "hop doubling label indexing for point to point distance querying on scale free networks"
  Abstract: We study the problem of point-to-point distance querying for massive scale-free graphs, which is important for numerous applications. Given a directed or undirected graph, we propose to build an index for answering such queries based on a hop-doubling labeling technique. We derive bounds on the index size, the computation costs and I/O costs based on the properties of unweighted scale-free graphs. We show that our method is much more efficient compared to the state-of-the-art technique, in terms of both querying time and indexing time. Our empirical study shows that our method can handle graphs that are orders of magnitude larger than existing methods.

Connected through hyperedge HE-1 (induced by source paper "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "gunrock a high performance graph processing library on the gpu"
    - "route planning in transportation networks"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "tree decompositions and social graphs"
    - "simple fast and scalable reachability oracle"
    - "hub accelerator fast and exact shortest path computation in large social networks"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-2 (induced by source paper "planting trees for scalable and efficient canonical hub labeling"):
  with members:
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-3 (induced by source paper "top k route search through submodularity modeling of recurrent poi features"):
  with members:
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "keyword aware optimal route search"

Connected through hyperedge HE-4 (induced by source paper "a highly scalable labelling approach for exact distance queries in complex networks"):
  with members:
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-5 (induced by source paper "pruning based distance sketches with provable guarantees on random graphs"):
  with members:
    - "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"
    - "algorithmic and hardness results for the hub labeling problem"
    - "on the complexity of hub labeling"
    - "sublinear distance labeling for sparse graphs"
    - "route planning in transportation networks"
    - "a scalable generative graph model with community structure"
    - "separating hierarchical and general hub labelings"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-6 (induced by source paper "topcom index for shortest distance query in directed graph"):
  with members:
    - "efficient single source shortest path and distance queries on large graphs"
    - "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"
    - "is label an independent set based labeling scheme for point to point distance querying on large graphs"

Connected through hyperedge HE-7 (induced by source paper "rehub extending hub labels for reverse k nearest neighbor queries on large scale networks"):
  with members:
    - "public transit labeling"
    - "route planning in transportation networks"
    - "salt a unified framework for all shortest path query variants on road networks"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "coloring large complex networks"):
  with members:
    - "graph sample and hold a framework for big graph analytics"
    - "estimating clique composition and size distributions from sampled network data"
    - "graph cluster randomization network exposure to multiple universes"
    - "dimension reduction via colour refinement"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "network sampling from static to streaming graphs"
    - "exact algorithms for maximum clique a computational study"
    - "exact algorithms for maximum clique a computational study"

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.DB(Databases)