human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "methods and experiences for developing abstractions for data intensive scientific applications"):
  Abstract: Developing software for scientific applications that require the integration of diverse types of computing, instruments and data present challenges that are distinct from commercial software, due to scale, heterogeneity, and the need to integrate various programming and computational models with evolving and heterogeneous infrastructure. Pervasive and effective abstractions are thus critical. The process of developing abstractions for scientific applications and infrastructures is not well understood. While theory-based approaches are suited for well-defined, closed environments, they have severe limitations for designing abstractions for complex, real-world systems. The design science research (DSR) method provides the basis for designing effective systems that can handle real-world complexities. DSR consists of two complementary phases: design and evaluation. This paper applies the DSR method to the development of abstractions for scientific applications. Specifically, we address the critical problem of distributed resource management on heterogeneous infrastructure, a challenge that currently limits many scientific applications. We use the pilot-abstraction, a widely used resource management abstraction for high-performance, high throughput, big data, and streaming applications, as a case study. We evaluate the activities of the process and extensively evaluate the artifacts using different methods, including conceptual modeling, performance characterizations, and modeling. We demonstrate the applicability of the DSR method for holistically handling the complexity of parallel and distributed computing environments addressing important application, system, and infrastructure challenges of scientific applications. Finally, we capture our experiences and formulate different lessons learned.

Includes members:
  Member "learning everywhere pervasive machine learning for effective high performance computation":
    also connected through other hyperedges:
      (none)
  Member "priority based parameter propagation for distributed dnn training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "performance characterization and modeling of serverless and hpc streaming applications":
    also connected through other hyperedges:
      (none)
  Member "synapse synthetic application profiler and emulator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "staged deployment of interactive multi application hpc workflows")
  Member "hadoop on hpc integrating hadoop and pilot based dynamic resource management":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "middleware building blocks for workflow systems")
  Member "on the hourglass model":
    also connected through other hyperedges:
      (none)
  Member "using pilot systems to execute many task workloads on supercomputers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "workflow design analysis for high resolution satellite image analysis")
  Member "a comprehensive perspective on pilot job systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serverless supercomputing high performance function as a service for science")

Question: Please tell me which class the source paper 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)