human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "building efficient query engines in a high level language"
  Abstract: Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance, instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend. In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes the entire query engine by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other. We evaluate our approach with the TPC-H benchmark and show that: (a) With all optimizations enabled, LegoBase significantly outperforms a commercial database and an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches. (c) The compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for compiling query engines.

Connected through hyperedge HE-1 (induced by source paper "multi layer optimizations for end to end data analytics"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "ac dc in database learning thunderstruck"
    - "efficient differentiable programming in a functional array processing language"
    - "stream fusion to completeness"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "hpat high performance analytics with scripting ease of use"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "learning models over relational data a brief tutorial"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "incremental techniques for large scale dynamic query processing"
    - "counting triangles under updates in worst case optimal time"
    - "on functional aggregate queries with additive inequalities"
    - "size bounds and query plans for relational joins"
    - "incremental view maintenance with triple lock factorization benefits"
    - "constraint solving via fractional edge covers"
    - "boolean tensor decomposition for conjunctive queries with negation"

Connected through hyperedge HE-3 (induced by source paper "a layered aggregate engine for analytics workloads"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "in database learning with sparse tensors"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"
    - "the ctu prague relational learning repository"
    - "mllib machine learning in apache spark"
    - "faq questions asked frequently"

Connected through hyperedge HE-4 (induced by source paper "compiling database application programs"):
  with members:
    - "automatic partitioning of database applications"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

Connected through hyperedge HE-5 (induced by source paper "plinycompute a platform for high performance distributed data intensive tool development"):
  with members:
    - "generating custom code for efficient query execution on heterogeneous processors"
    - "flare native compilation for heterogeneous workloads in apache spark"
    - "lifetime based memory management for distributed data processing systems"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-6 (induced by source paper "macrobase prioritizing attention in fast data"):
  with members:
    - "sketching linear classifiers over data streams"
    - "drop dimensionality reduction optimization for time series"
    - "asap prioritizing attention via time series smoothing"
    - "noscope optimizing neural network queries over video at scale"
    - "incremental knowledge base construction using deepdive"
    - "graph based anomaly detection and description a survey"
    - "towards a unified architecture for in rdbms analytics"
    - "identifying the signs of fraudulent accounts using data mining techniques"

Question: Please tell me which class the center node 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)