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: "rheemix in the data jungle a cross platform query optimizer"
  Abstract: In pursuit of efficient and scalable data analytics, the insight that "one size does not fit all" has given rise to a plethora of specialized data processing platforms and today's complex data analytics are moving beyond the limits of a single platform. To cope with these new requirements, we present a cross-platform optimizer that allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i)~a mechanism based on graph transformations to explore alternative execution strategies; (ii)~a novel graph-based approach to efficiently plan data movement among subtasks and platforms; and (iii)~an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. The results show that our optimizer is capable of selecting the most efficient platform combination for a given task, freeing data analysts from the need to choose and orchestrate platforms. In particular, our optimizer allows certain tasks to run more than one order of magnitude faster than on state-of-the-art platforms, such as Spark.

Connected through hyperedge HE-1 (induced by source paper "polystore accelerated polystore system for heterogeneous workloads"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "in rdbms hardware acceleration of advanced analytics"
    - "practical design space exploration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "empowering in memory relational database engines with native graph processing"
    - "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
    - "pipegen data pipe generator for hybrid analytics"
    - "the bigdawg polystore system and architecture"

Connected through hyperedge HE-2 (induced by source paper "building your cross platform application with rheem"):
  with members:
    - "a cost based optimizer for gradient descent optimization"
    - "pipegen data pipe generator for hybrid analytics"
    - "tensorflow a system for large scale machine learning"
    - "how to use big data technologies to optimize operations in upstream petroleum industry"

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)