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: "two particle in grid realisations on spacetrees"
  Abstract: We introduce two algorithms to hold particles in dynamically adaptive grids.Particles are associated to the nearest cell or vertex though they may tunnel.A PIC code validates that particle-grid interactions can be realised efficiently.Measurements reveal which variant performs better for certain setups and hardware.A data movement prediction scheme avoids all-to-all communication partially. The present paper studies two particle management strategies for dynamically adaptive Cartesian grids at hands of a particle-in-cell code. One holds the particles within the grid cells, the other within the grid vertices. The fundamental challenge for the algorithmic strategies results from the fact that particles may run through the grid without velocity constraints. To facilitate this, we rely on multiscale grid representations. They allow us to lift and drop particles between different spatial resolutions. We call this cell-based strategy particle in tree (PIT). Our second approach assigns particles to vertices describing a dual grid (PIDT) and augments the lifts and drops with multiscale linked cells.Our experiments validate the two schemes at hands of an electrostatic particle-in-cell code by retrieving the dispersion relation of Langmuir waves in a thermal plasma. They reveal that different particle and grid characteristics favour different realisations. The possibility that particles can tunnel through an arbitrary number of grid cells implies that most data is exchanged between neighbouring ranks, while very few data is transferred non-locally. This constraints the scalability as the code potentially has to realise global communication. We show that the merger of an analysed tree grammar with PIDT allows us to predict particle movements among several levels and to skip parts of this global communication a priori. It is capable to outperform several established implementations based upon trees and/or space-filling curves.

Connected through hyperedge HE-1 (induced by source paper "the peano software parallel automaton based dynamically adaptive grid traversals"):
  with members:
    - "stop talking to me a communication avoiding ader dg realisation"
    - "quasi matrix free hybrid multigrid on dynamically adaptive cartesian grids"
    - "complex additive geometric multilevel solvers for helmholtz equations on spacetrees"
    - "algebraic geometric matrix free multigrid on dynamically adaptive cartesian meshes"
    - "how many three dimensional hilbert curves are there"
    - "morton curve segments produce no more than two distinct face connected subdomains"
    - "bounds on the number of discontinuities of morton type space filling curves"
    - "a survey of high level frameworks in block structured adaptive mesh refinement packages"

Connected through hyperedge HE-2 (induced by source paper "a high level characterisation and generalisation of communication avoiding programming techniques"):
  with members:
    - "parallel performance of algebraic multigrid domain decomposition amg dd"
    - "stop talking to me a communication avoiding ader dg realisation"
    - "enclave tasking for discontinuous galerkin methods on dynamically adaptive meshes"
    - "algebraic geometric matrix free multigrid on dynamically adaptive cartesian meshes"
    - "swift using task based parallelism fully asynchronous communication and graph partition based domain decomposition for strong scaling on more than 100 000 cores"
    - "asynchronous mpi for the masses"

Connected through hyperedge HE-3 (induced by source paper "exahype an engine for parallel dynamically adaptive simulations of wave problems"):
  with members:
    - "the peano software parallel automaton based dynamically adaptive grid traversals"
    - "quasi matrix free hybrid multigrid on dynamically adaptive cartesian grids"

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.DS(Data Structures and Algorithms)