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: "dagger a scalable index for reachability queries in large dynamic graphs"
  Abstract: With the ubiquity of large-scale graph data in a variety of application domains, querying them effectively is a challenge. In particular, reachability queries are becoming increasingly important, especially for containment, subsumption, and connectivity checks. Whereas many methods have been proposed for static graph reachability, many real-world graphs are constantly evolving, which calls for dynamic indexing. In this paper, we present a fully dynamic reachability index over dynamic graphs. Our method, called DAGGER, is a light-weight index based on interval labeling, that scales to million node graphs and beyond. Our extensive experimental evaluation on real-world and synthetic graphs confirms its effectiveness over baseline methods.

Connected through hyperedge HE-1 (induced by source paper "efficient processing of reachability and time based path queries in a temporal graph"):
  with members:
    - "time varying graphs and social network analysis temporal indicators and metrics"

Connected through hyperedge HE-2 (induced by source paper "a survey on methods and systems for graph compression"):
  with members:
    - "preach a fast lightweight reachability index using pruning and contraction hierarchies"
    - "scalable rdf data compression using x10"
    - "graphchi db simple design for a scalable graph database system on just a pc"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-3 (induced by source paper "supporting window analytics over large scale dynamic graphs"):
  with members:
    - "eagr supporting continuous ego centric aggregate queries over large dynamic graphs"
    - "optimization of analytic window functions"
    - "k reach who is in your small world"

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)