human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"):
  Abstract: Graph processing has become an important part of multiple areas of computer science, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Numerous graphs such as web or social networks may contain up to trillions of edges. Often, these graphs are also dynamic (their structure changes over time) and have domain-specific rich data associated with vertices and edges. Graph database systems such as Neo4j enable storing, processing, and analyzing such large, evolving, and rich datasets. Due to the sheer size of such datasets, combined with the irregular nature of graph processing, these systems face unique design challenges. To facilitate the understanding of this emerging domain, we present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., triple stores, tuple stores, native graph database systems, or object-oriented systems), the associated graph models (e.g., RDF or Labeled Property Graph), data organization techniques (e.g., storing graph data in indexing structures or dividing data into records), and different aspects of data distribution and query execution (e.g., support for sharding and ACID). 45 graph database systems are presented and compared, including Neo4j, OrientDB, or Virtuoso. We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we describe research and engineering challenges to outline the future of graph databases.

Includes members:
  Member "graph processing on fpgas taxonomy survey challenges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "slim fly a cost effective low diameter network topology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "active access a mechanism for high performance distributed data centric computations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "network accelerated non contiguous memory transfers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "slim graph practical lossy graph compression for approximate graph processing storage and analytics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "survey and taxonomy of lossless graph compression and space efficient graph representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structural indexing for conjunctive path queries")
  Member "high level programming abstractions for distributed graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")

Question: Please tell me which class the source paper 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)