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 "laradb a minimalist kernel for linear and relational algebra computation"):
  Abstract: Analytics tasks manipulate structured data with variants of relational algebra (RA) and quantitative data with variants of linear algebra (LA). The two computational models have overlapping expressiveness, motivating a common programming model that affords unified reasoning and algorithm design. At the logical level we propose LARA, a lean algebra of three operators, that expresses RA and LA as well as relevant optimization rules. We show a series of proofs that position LARA at just the right level of expressiveness for a middleware algebra: more explicit than MapReduce but more general than RA or LA. At the physical level we find that the LARA operators afford efficient implementations using a single primitive that is available in a variety of backend engines: range scans over partitioned sorted maps.   To evaluate these ideas, we implemented the LARA operators as range iterators in Apache Accumulo, a popular implementation of Google's BigTable. First we show how LARA expresses a sensor quality control task, and we measure the performance impact of optimizations LARA admits on this task. Second we show that the LARADB implementation outperforms Accumulo's native MapReduce integration on a core task involving join and aggregation in the form of matrix multiply, especially at smaller scales that are typically a poor fit for scale-out approaches. We find that LARADB offers a conceptually lean framework for optimizing mixed-abstraction analytics tasks, without giving up fast record-level updates and scans.

Includes members:
  Member "lara a key value algebra underlying arrays and relations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards scalable dataframe systems")
  Member "associative array model of sql nosql and newsql databases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ai data wrangling with associative arrays")
  Member "mathematical foundations of the graphblas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices")
  Member "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a graphblas approach for subgraph counting")
  Member "using a power law distribution to describe big data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ai enabling technologies a survey")
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "graphulo implementation of server side sparse matrix multiply in the accumulo database":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "streaming 1 9 billion hypersparse network updates per second with d4m")
  Member "graphulo linear algebra graph kernels for nosql databases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a systematic survey of general sparse matrix 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)