human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "accelerating a fluvial incision and landscape evolution model with parallelism"):
  Abstract: Abstract   Solving inverse problems, performing sensitivity analyses, and achieving statistical rigour in landscape evolution models require running many model realizations. Parallel computation is necessary to achieve this in a reasonable time. However, no previous landscape evolution algorithm is able to leverage modern parallelism. Here, I describe an algorithm that can utilize the parallel potential of GPUs and many-core processors, in addition to working well in serial. The new algorithm runs 43× faster (70 s vs. 3000 s on a 10,000×10,000 input) than the previous state-of-the-art and exhibits sublinear scaling with input size. I also identify key techniques for multiple flow direction routing and quickly eliminating landscape depressions and local minima. Complete, well-commented, easily adaptable source code for all versions of the algorithm is available on Github and Zenodo.

Includes members:
  Member "parallel non divergent flow accumulation for trillion cell digital elevation models on desktops or clusters":
    also connected through other hyperedges:
      (none)
  Member "parallel priority flood depression filling for trillion cell digital elevation models on desktops or clusters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parallel non divergent flow accumulation for trillion cell digital elevation models on desktops or clusters")
  Member "an efficient assignment of drainage direction over flat surfaces in raster digital elevation models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parallel non divergent flow accumulation for trillion cell digital elevation models on desktops or clusters")
  Member "priority flood an optimal depression filling and watershed labeling algorithm for digital elevation models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parallel non divergent flow accumulation for trillion cell digital elevation models on desktops or clusters")

Question: Please tell me which class the source paper 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.CE(Computational Engineering, Finance, and Science)