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 priority flood depression filling for trillion cell digital elevation models on desktops or clusters"
  Abstract: Algorithms for extracting hydrologic features and properties from digital elevation models (DEMs) are challenged by large datasets, which often cannot fit within a computer's RAM. Depression filling is an important preconditioning step to many of these algorithms. Here, I present a new, linearly scaling algorithm which parallelizes the Priority-Flood depression-filling algorithm by subdividing a DEM into tiles. Using a single-producer, multi-consumer design, the new algorithm works equally well on one core, multiple cores, or multiple machines and can take advantage of large memories or cope with small ones. Unlike previous algorithms, the new algorithm guarantees a fixed number of memory access and communication events per subdivision of the DEM. In comparison testing, this results in the new algorithm running generally faster while using fewer resources than previous algorithms. For moderately sized tiles, the algorithm exhibits ~60% strong and weak scaling efficiencies up to 48cores, and linear time scaling across datasets ranging over three orders of magnitude. The largest dataset on which I run the algorithm has 2 trillion (2×1012) cells. With 48cores, processing required 4.8h wall-time (9.3 compute-days). 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 for download from a repository. HighlightsI present a parallelized Priority-Flood algorithm to fill depressions in large DEMs.Its time scales linearly with 60% strong and weak scaling efficiency over 48cores.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 non divergent flow accumulation 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"

Connected through hyperedge HE-2 (induced by source paper "parallel non divergent flow accumulation for trillion cell digital elevation models on desktops or clusters"):
  with members:
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a scalable approach for tree segmentation within small footprint airborne lidar data"):
  with members:
    - "a robust approach for tree segmentation in deciduous forests using small footprint airborne lidar data"

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)