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: "parallel non divergent flow accumulation for trillion cell digital elevation models on desktops or clusters"
  Abstract: Continent-scale datasets challenge hydrological algorithms for processing digital elevation models. Flow accumulation is an important input for many such algorithms; here, I parallelize its calculation. The new algorithm works on one or many cores, or multiple machines, and can take advantage of large memories or cope with small ones. Unlike previous parallel algorithms, the new algorithm guarantees a fixed number of memory access and communication events per raster cell. In testing, the new algorithm ran faster and used fewer resources than previous algorithms, exhibiting 30% strong and weak scaling efficiencies up to 48 cores and linear scaling across datasets ranging over three orders of magnitude. The largest dataset tested had two trillion (21012) cells. With 48 cores, processing required 24min wall-time (14.5 compute-hours). This test is three orders of magnitude larger than any previously performed in the literature. Complete, well-commented source code and correctness tests are available on Github. I present a parallelized ow accumulation algorithm for large DEMs.The algorithm guarantees fixed numbers of memory and communication events.The algorithm runs in serial on a standard desktop or in parallel on a supercomputer.The algorithm performs tests three orders of magnitude larger than any previous work.Well-commented source code and an automated test suite are available on Github.

Connected through hyperedge HE-1 (induced by source paper "accelerating a fluvial incision and landscape evolution model with parallelism"):
  with members:
    - "parallel priority flood depression filling for trillion cell digital elevation models on desktops or clusters"
    - "an efficient assignment of drainage direction over flat surfaces in raster digital elevation models"
    - "priority flood an optimal depression filling and watershed labeling algorithm for digital elevation models"

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)