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: "flexible composition and execution of high performance high fidelity multiscale biomedical simulations"
  Abstract: Multiscale simulations are essential in the biomedical domain to accurately model human physiology. We present a modular approach for designing, constructing and executing multiscale simulations on a wide range of resources, from laptops to petascale supercomputers, including combinations of these. Our work features two multiscale applications, in-stent restenosis and cerebrovascular bloodflow, which combine multiple existing single-scale applications to create a multiscale simulation. These applications can be efficiently coupled, deployed and executed on computers up to the largest (peta) scale, incurring a coupling overhead of 1–10% of the total execution time.

Connected through hyperedge HE-1 (induced by source paper "deepdownscale a deep learning strategy for high resolution weather forecast"):
  with members:
    - "resnet in resnet generalizing residual architectures"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "raisr rapid and accurate image super resolution"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "multiscale computing in the exascale era"):
  with members:
    - "exploiting data representation for fault tolerance"
    - "fabsim facilitating computational research through automation on large scale and distributed e infrastructures"
    - "an automated multiscale ensemble simulation approach for vascular blood flow"
    - "petascale turbulence simulation using a highly parallel fast multipole method on gpus"
    - "survey of multiscale and multiphysics applications and communities"

Connected through hyperedge HE-3 (induced by source paper "an automated multiscale ensemble simulation approach for vascular blood flow"):
  with members:
    - "weighted decomposition in high performance lattice boltzmann simulations are some lattice sites more equal than others"
    - "mpwide a light weight library for efficient message passing over wide area networks"
    - "impact of blood rheology on wall shear stress in a model of the middle cerebral artery"

Connected through hyperedge HE-4 (induced by source paper "weighted decomposition in high performance lattice boltzmann simulations are some lattice sites more equal than others"):
  with members:
    - "coalesced communication a design pattern for complex parallel scientific software"

Connected through hyperedge HE-5 (induced by source paper "mpwide a light weight library for efficient message passing over wide area networks"):
  with members:
    - "high performance distributed multi model multi kernel simulations a case study in jungle computing"
    - "high performance cosmological simulations on a grid of supercomputers"
    - "high performance gravitational n body simulations on a planet wide distributed supercomputer"
    - "a lightweight communication library for distributed computing"

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)