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: "flare native compilation for heterogeneous workloads in apache spark"
  Abstract: The need for modern data analytics to combine relational, procedural, and map-reduce-style functional processing is widely recognized. State-of-the-art systems like Spark have added SQL front-ends and relational query optimization, which promise an increase in expressiveness and performance. But how good are these extensions at extracting high performance from modern hardware platforms? #R##N#While Spark has made impressive progress, we show that for relational workloads, there is still a significant gap compared with best-of-breed query engines. And when stepping outside of the relational world, query optimization techniques are ineffective if large parts of a computation have to be treated as user-defined functions (UDFs). #R##N#We present Flare: a new back-end for Spark that brings performance closer to the best SQL engines, without giving up the added expressiveness of Spark. We demonstrate order of magnitude speedups both for relational workloads such as TPC-H, as well as for a range of machine learning kernels that combine relational and iterative functional processing. #R##N#Flare achieves these results through (1) compilation to native code, (2) replacing parts of the Spark runtime system, and (3) extending the scope of optimization and code generation to large classes of UDFs.

Connected through hyperedge HE-1 (induced by source paper "on optimizing operator fusion plans for large scale machine learning in systemml"):
  with members:
    - "weld rethinking the interface between data intensive applications"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "tensorflow a system for large scale machine learning"
    - "faq questions asked frequently"
    - "julia a fresh approach to numerical computing"
    - "conditioning probabilistic databases"

Connected through hyperedge HE-2 (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"
    - "building efficient query engines in a high level language"
    - "lifetime based memory management for distributed data processing systems"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"

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)