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: "an efficient assignment of drainage direction over flat surfaces in raster digital elevation models"
  Abstract: In processing raster digital elevation models (DEMs) it is often necessary to assign drainage directions over flats-that is, over regions with no local elevation gradient. This paper presents an approach to drainage direction assignment which is not restricted by a flat's shape, number of outlets, or surrounding topography. Flow is modeled by superimposing a gradient away from higher terrain with a gradient towards lower terrain resulting in a drainage field exhibiting flow convergence, an improvement over methods which produce regions of parallel flow. This approach builds on previous work by Garbrecht and Martz (1997), but presents several important improvements. The improved algorithm guarantees that flats are only resolved if they have outlets. The algorithm does not require iterative application; a single pass is sufficient to resolve all flats. The algorithm presents a clear strategy for identifying flats and their boundaries. The algorithm is not susceptible to loss of floating-point precision. Furthermore, the algorithm is efficient, operating in O(N) time whereas the older algorithm operates in O ( N 3 / 2 ) time. In testing, the improved algorithm ran 6.5 times faster than the old for a 100i?100 cell flat and 69 times faster for a 700i?700 cell flat. In tests on actual DEMs, the improved algorithm finished its processing 38-110 times sooner while running on a single processor than a parallel implementation of the old algorithm did while running on 16 processors. The improved algorithm is an optimal, accurate, easy-to-implement drop-in replacement for the original. Pseudocode is provided in the paper and working source code is provided in the Supplemental Materials. HighlightsWe present an improved algorithm to model ow directions in at regions of DEMs.The algorithm works regardless of the topography surrounding the at region.The algorithm produces convergent ows away from higher and towards lower terrain.The algorithm has a number of sanity checks which guarantee correct output.The algorithm works in O(N) time and supplants an older O(N3\2) time algorithm.

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"
    - "parallel priority flood depression filling for trillion cell digital elevation models on desktops or clusters"
    - "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:
    - "parallel priority flood depression filling for trillion cell digital elevation models on desktops or clusters"
    - "priority flood an optimal depression filling and watershed labeling algorithm for digital elevation models"

Connected through hyperedge HE-3 (induced by source paper "parallel priority flood depression filling for trillion cell digital elevation models on desktops or clusters"):
  with members:
    - "distributed parallel d8 up slope area calculation in 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.DS(Data Structures and Algorithms)