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: "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
  Abstract: High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications. Consequently, they have been architected as producers and not consumers of data. The Apache Hadoop ecosystem has evolved to meet the requirements of data processing applications and has addressed many of the limitations of HPC platforms. There exist a class of scientific applications however, that need the collective capabilities of traditional high-performance computing environments and the Apache Hadoop ecosystem. For example, the scientific domains of bio-molecular dynamics, genomics and network science need to couple traditional computing with Hadoop/Spark based analysis. We investigate the critical question of how to present the capabilities of both computing environments to such scientific applications. Whereas this questions needs answers at multiple levels, we focus on the design of resource management middleware that might support the needs of both. We propose extensions to the Pilot-Abstraction to provide a unifying resource management layer. This is an important step that allows applications to integrate HPC stages (e.g. simulations) to data analytics. Many supercomputing centers have started to officially support Hadoop environments, either in a dedicated environment or in hybrid deployments using tools such as myHadoop. This typically involves many intrinsic, environment-specific details that need to be mastered, and often swamp conceptual issues like: How best to couple HPC and Hadoop application stages? How to explore runtime trade-offs (data localities vs. data movement)? This paper provides both conceptual understanding and practical solutions to the integrated use of HPC and Hadoop environments.

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"
    - "on the hourglass model"
    - "using pilot systems to execute many task workloads on supercomputers"
    - "a comprehensive perspective on pilot job systems"
    - "a tale of two data intensive paradigms applications abstractions and architectures"

Connected through hyperedge HE-2 (induced by source paper "middleware building blocks for workflow systems"):
  with members:
    - "microservices yesterday today and tomorrow"
    - "extasy scalable and flexible coupling of md simulations and advanced sampling techniques"
    - "a comprehensive perspective on pilot job systems"
    - "scientific computing meets big data technology an astronomy use case"

Connected through hyperedge HE-3 (induced by source paper "evaluation of pilot jobs for apache spark applications on hpc clusters"):
  with members:
    - "comparative evaluation of big data systems on scientific image analytics workloads"
    - "a comprehensive perspective on pilot job systems"
    - "radical pilot scalable execution of heterogeneous and dynamic workloads on supercomputers"

Connected through hyperedge HE-4 (induced by source paper "task parallel analysis of molecular dynamics trajectories"):
  with members:
    - "high throughput binding affinity calculations at extreme scales"
    - "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"
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "p a model of pilot abstractions"

Connected through hyperedge HE-5 (induced by source paper "pilot streaming a stream processing framework for high performance computing"):
  with members:
    - "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"
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "p a model of pilot abstractions"

Connected through hyperedge HE-6 (induced by source paper "d space4cloud a design tool for big data applications"):
  with members:
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "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)