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 "memory efficient group by aggregates over multi way joins"):
  Abstract: Aggregate computation in relational databases has long been done using the standard unary aggregation and binary join operators. These implement the classical model of computing joins between relations two at a time, materializing the intermediate results, and then proceeding to do either sort-based or hash-based aggregate computation to derive the final result. This approach, however, can be dramatically sub-optimal in the case of low-selectivity joins, and often ends up generating large intermediate results even if the relations involved as well as the final result sets themselves are quite small. Moreover, many of the computed intermediate results may never be part of the final result. In this paper we propose a novel aggregate query processing technique that leverages graph data structures towards efficiently answering aggregate queries over joins, without materializing intermediate join results. We wrap this technique inside a multi-way composite database operator called JOIN-AGG that combines Join and Aggregation. We provide a general framework for executing aggregation queries over arbitrary acyclic joins, involving any number of group-by attributes from any relation. We also present a thorough experimental evaluation on both real world and synthetic datasets. Our experiments show that our operators can achieve orders of magnitude lower query times and memory requirements compared to the traditional approach, even when implemented outside of the database system.

Includes members:
  Member "ac dc in database learning thunderstruck":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "on functional aggregate queries with additive inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning models over relational data a brief tutorial")
  Member "covers of query results":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constant delay enumeration with fpt preprocessing for conjunctive queries of bounded submodular width")
  Member "boolean tensor decomposition for conjunctive queries with negation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning models over relational data a brief tutorial")
  Member "worst case optimal algorithms for parallel query processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "instance and output optimal parallel algorithms for acyclic joins")
  Member "aggregations over generalized hypertree decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tree projections and constraint optimization problems fixed parameter tractability and parallel algorithms")
  Member "faq questions asked frequently":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "skew strikes back new developments in the theory of join algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast join project query evaluation using matrix multiplication")

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)