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: "comparative evaluation of big data systems on scientific image analytics workloads"
  Abstract: Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear, however, how well these systems support real-world image analysis use cases, and how performant are the image analytics tasks implemented on top of such systems. In this paper, we present the first comprehensive evaluation of large-scale image analysis systems using two real-world scientific image data processing use cases. We evaluate five representative systems (SciDB, Myria, Spark, Dask, and TensorFlow) and find that each of them has shortcomings that complicate implementation or hurt performance. Such shortcomings lead to new research opportunities in making large-scale image analysis both efficient and easy to use.

Connected through hyperedge HE-1 (induced by source paper "evaluation of pilot jobs for apache spark applications on hpc clusters"):
  with members:
    - "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
    - "a comprehensive perspective on pilot job systems"
    - "radical pilot scalable execution of heterogeneous and dynamic workloads on supercomputers"

Connected through hyperedge HE-2 (induced by source paper "a performance comparison of dask and apache spark for data intensive neuroimaging pipelines"):
  with members:
    - "performance evaluation of big data processing strategies for neuroimaging"

Connected through hyperedge HE-3 (induced by source paper "cuttlefish a lightweight primitive for adaptive query processing"):
  with members:
    - "distributed clustering of linear bandits in peer to peer networks"
    - "on context dependent clustering of bandits"
    - "caffe con troll shallow ideas to speed up deep learning"
    - "mllib machine learning in apache spark"
    - "show attend and tell neural image caption generation with visual attention"
    - "on the prior sensitivity of thompson sampling"
    - "rethinking the inception architecture for computer vision"
    - "online clustering of bandits"

Connected through hyperedge HE-4 (induced by source paper "performance evaluation of big data processing strategies for neuroimaging"):
  with members:
    - "task parallel analysis of molecular dynamics trajectories"
    - "fast and scalable distributed deep convolutional autoencoder for fmri big data analytics"

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)