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 "fast in memory sql analytics on relationships between entities"):
  Abstract: In this paper, we study relationship queries on graph databases with binary relationship. A relationship query is a graph reachability query bounded on primary-keys and foreign-keys with aggregation on single output attribute. Both row stores and column stores miss key opportunities towards the efficient execution of relationship queries, making them unacceptably slow in real-world OLAP scenarios. #R##N#We present the FastR in-memory analytics engine that utilizes a new form of bottom-up fully pipelined query processing execution strategy. The plans run on a novel data organization that combines salient features of column-based organization, indexing and compression. Furthermore, FastR compiles its query plans into query-aware executable C++ source codes. Besides achieving runtime efficiency, FastR also reduces main memory requirements because, unlike column databases, FastR selectively allows more dense forms of compression including heavy-weighted compressions, which do not support random access. #R##N#In our experiments, we used FastR to accelerate queries for two OLAP dashboards in the biomedical field. The first dashboard runs queries on the PubMed dataset and the second one on the SemMedDB dataset. FastR outperforms Postgres by 2-4 orders of magnitude and MonetDB by 1-3 orders of magnitude, when all of them are running on RAM. Our experiments dissect the FastR advantage between (i) the novel FastR execution strategy and associated data structures and (ii) the use of compiled code. We also provide an analysis and experiments illustrating space savings due to appropriate use of compression methods. Finally, we investigate the beneficial effect of the increasing CPU cycles / RAM transfer rate ratio on the space-time tradeoff for various compression methods used in FastR.

Includes members:
  Member "high throughput transaction executions on graphics processors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient memory management for gpu based deep learning systems")
  Member "sorting improves word aligned bitmap indexes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "roaring bitmaps implementation of an optimized software library")

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)