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 "providing insights for queries affected by failures and stragglers"):
  Abstract: Interactive time responses are a crucial requirement for users analyzing large amounts of data. Such analytical queries are typically run in a distributed setting, with data being sharded across thousands of nodes for high throughput. However, providing real-time analytics is still a very big challenge; with data distributed across thousands of nodes, the probability that some of the required nodes are unavailable or very slow during query execution is very high and unavailability may result in slow execution or even failures. The sheer magnitude of data and users increase resource contention and this exacerbates the phenomenon of stragglers and node failures during execution. In this paper, we propose a novel solution to alleviate the straggler/failure problem that exploits existing efficient partitioning properties of the data, particularly, co-hash partitioned data, and provides approximate answers along with confidence bounds to queries affected by failed/straggler nodes. We consider aggregate queries that involve joins, group bys, having clauses and a subclass of nested subqueries. Finally, we validate our approach through extensive experiments on the TPC-H dataset.

Includes members:
  Member "shark sql and rich analytics at scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "knowledge graphs")
  Member "blinkdb queries with bounded errors and bounded response times on very large data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ml aqp query driven approximate query processing based on machine learning")
  Member "fdb a query engine for factorised relational databases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tuple oriented compression for large scale mini batch stochastic gradient descent")

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)