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: "accelerating dissipative particle dynamics simulations on gpus algorithms numerics and applications"
  Abstract: Abstract   We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10–30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster  in situ  generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases simulating Poiseuille flow and spontaneous vesicle formation. Computer benchmarks demonstrate the speedup of our implementation over the CPU implementation as well as strong and weak scalability. A large-scale simulation of spontaneous vesicle formation consisting of 128 million particles was conducted to further illustrate the practicality of our code in real-world applications.    Program summary   Program title:  GPU-accelerated DPD Package for LAMMPS   Catalogue identifier:  AETN_v1_0   Program summary URL:  http://cpc.cs.qub.ac.uk/summaries/AETN_v1_0.html    Program obtainable from:  CPC Program Library, Queen’s University, Belfast, N. Ireland   Licensing provisions:  GNU General Public License, version 3   No. of lines in distributed program, including test data, etc.:  1 602 716   No. of bytes in distributed program, including test data, etc.:  26 489 166   Distribution format:  tar.gz   Programming language:  C/C++, CUDA C/C++, MPI.   Computer:  Any computers having nVidia GPGPUs with compute capability 3.0.   Operating system:  Linux.   Has the code been vectorized or parallelized?:  Yes. Number of processors used: 1024 16-core CPUs and 1024 GPUs   RAM:  500 Mbytes host memory, 2 Gbytes device memory   Supplementary material:  The data for the examples discussed in the manuscript is available for download.   Classification:  6.5, 12, 16.1, 16.11.   Nature of problem:   Particle-based simulation of mesoscale systems involving nano/micro-fluids, polymers and spontaneous self-assembly process.   Solution method:   The system is approximated by a number of coarse-grained particles interacting through pairwise potentials and bonded potentials. Classical mechanics is assumed following Newton’s laws. The evolution of the system is integrated using a time-stepping scheme such as Velocity-Verlet.   Restrictions:   The code runs only on CUDA GPGPUs with compute capability 3.0.   Unusual features:   Fully implemented on GPGPUs with significant speedup.   Running time:   78 h using 1024 GPGPUs for simulating a 128-million-particle system for 18.4 million time steps.

Connected through hyperedge HE-1 (induced by source paper "a high throughput solver for marginalized graph kernels on gpu"):
  with members:
    - "prediction of atomization energy using graph kernel and active learning"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"

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)