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: "using pilot systems to execute many task workloads on supercomputers"
  Abstract: High performance computing systems have historically been designed to support applications comprised of mostly monolithic, single-job workloads. Pilot systems decouple workload specification, resource selection, and task execution via job placeholders and late-binding. Pilot systems help to satisfy the resource requirements of workloads comprised of multiple tasks. RADICAL-Pilot (RP) is a modular and extensible Python-based pilot system. In this paper we describe RP's design, architecture and implementation, and characterize its performance. RP is capable of spawning more than 100 tasks/second and supports the steady-state execution of up to 16K concurrent tasks. RP can be used stand-alone, as well as integrated with other application-level tools as a runtime system.

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"
    - "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 "workflow design analysis for high resolution satellite image analysis"):
  with members:
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "right whale recognition using convolutional neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "deepdrivemd deep learning driven adaptive molecular simulations for protein folding"):
  with members:
    - "radical cybertools middleware building blocks for scalable science"
    - "understanding ml driven hpc applications and infrastructure"
    - "characterizing the performance of executing many tasks on summit"
    - "ensemble toolkit scalable and flexible execution of ensembles of tasks"
    - "a comprehensive perspective on pilot job systems"

Connected through hyperedge HE-4 (induced by source paper "radical cybertools middleware building blocks for scalable science"):
  with members:
    - "rapid concurrent and adaptive extreme scale binding free energy calculation"
    - "extasy scalable and flexible coupling of md simulations and advanced sampling techniques"
    - "repex a flexible framework for scalable replica exchange molecular dynamics simulations"
    - "integrating abstractions to enhance the execution of distributed applications"

Connected through hyperedge HE-5 (induced by source paper "characterizing the performance of executing many tasks on summit"):
  with members:
    - "middleware building blocks for workflow systems"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "implementing adaptive ensemble biomolecular applications at scale"):
  with members:
    - "ensemble toolkit scalable and flexible execution of ensembles of tasks"

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)