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: "a tale of two data intensive paradigms applications abstractions and architectures"
  Abstract: Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.

Connected through hyperedge HE-1 (induced by source paper "methods and experiences for developing abstractions for data intensive scientific applications"):
  with members:
    - "learning everywhere pervasive machine learning for effective high performance computation"
    - "priority based parameter propagation for distributed dnn training"
    - "performance characterization and modeling of serverless and hpc streaming applications"
    - "synapse synthetic application profiler and emulator"
    - "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
    - "on the hourglass model"
    - "using pilot systems to execute many task workloads on supercomputers"
    - "a comprehensive perspective on pilot job systems"

Connected through hyperedge HE-2 (induced by source paper "task parallel analysis of molecular dynamics trajectories"):
  with members:
    - "high throughput binding affinity calculations at extreme scales"
    - "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
    - "extasy scalable and flexible coupling of md simulations and advanced sampling techniques"
    - "repex a flexible framework for scalable replica exchange molecular dynamics simulations"
    - "using pilot systems to execute many task workloads on supercomputers"
    - "a comprehensive perspective on pilot job systems"
    - "mllib machine learning in apache spark"
    - "p a model of pilot abstractions"

Connected through hyperedge HE-3 (induced by source paper "pilot streaming a stream processing framework for high performance computing"):
  with members:
    - "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
    - "extasy scalable and flexible coupling of md simulations and advanced sampling techniques"
    - "radical pilot scalable execution of heterogeneous and dynamic workloads on supercomputers"
    - "mllib machine learning in apache spark"
    - "p a model of pilot abstractions"

Connected through hyperedge HE-4 (induced by source paper "an experimental survey on big data frameworks"):
  with members:
    - "bladyg a graph processing framework for large dynamic graphs"
    - "mr simlab scalable subgraph selection with label similarity for big data"
    - "big graph mining frameworks and techniques"
    - "mli an api for distributed machine learning"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-5 (induced by source paper "performance evaluation of an algorithm based asynchronous checkpoint restart fault tolerant application using mixed mpi gpi 2"):
  with members:
    - "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies"
    - "building a fault tolerant application using the gaspi communication layer"

Connected through hyperedge HE-6 (induced by source paper "hiframes high performance data frames in a scripting language"):
  with members:
    - "automatic parallelization executing sequential programs on a task based parallel runtime"
    - "hpat high performance analytics with scripting ease of use"

Connected through hyperedge HE-7 (induced by source paper "big data frameworks a comparative study"):
  with members:
    - "bladyg a graph processing framework for large dynamic graphs"
    - "mr simlab scalable subgraph selection with label similarity for big data"
    - "big graph mining frameworks and techniques"
    - "mli an api for distributed machine learning"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-8 (induced by source paper "d space4cloud a design tool for big data applications"):
  with members:
    - "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
    - "survey on improved scheduling in hadoop mapreduce in cloud environments"

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.DC(Distributed, Parallel, and Cluster Computing)