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: "experimental study of compressed stack algorithms in limited memory environments"
  Abstract: The {\em compressed stack} is a data structure designed by Barba {\em et al.} (Algorithmica 2015) that allows to reduce the amount of memory needed by an algorithm (at the cost of increasing its runtime). In this paper we introduce the first implementation of this data structure and make its source code publicly available. #R##N#Together with the implementation we analyze the performance of the compressed stack. In our synthetic experiments, considering different test scenarios and using data sizes ranging up to $2^{30}$ elements, we compare it with the classic (uncompressed) stack, both in terms of runtime and memory used. #R##N#Our experiments show that the compressed stack needs significantly less memory than the usual stack (this difference is significant for inputs containing $2000$ or more elements). Overall, with a proper choice of parameters, we can save a significant amount of space (from two to four orders of magnitude) with a small increase in the runtime ($2.32$ times slower on average than the classic stack). These results holds even in test scenarios specifically designed to be challenging for the compressed stack.

Connected through hyperedge HE-1 (induced by source paper "an experimental study of algorithms for geodesic shortest paths in the constant workspace model"):
  with members:
    - "computational geometry column 67"
    - "shortest path in a polygon using sublinear space"
    - "memory constrained algorithms for simple polygons"

Connected through hyperedge HE-2 (induced by source paper "computational geometry column 67"):
  with members:
    - "common tangents of two disjoint polygons in linear time and constant workspace"
    - "time space trade off for finding the k visibility region of a point in a polygon"
    - "an experimental study of algorithms for geodesic shortest paths in the constant workspace model"
    - "improved time space trade offs for computing voronoi diagrams"
    - "ime space trade offs for computing euclidean minimum spanning trees"
    - "time space trade offs for triangulations and voronoi diagrams"
    - "outer common tangents and nesting of convex hulls in linear time and constant workspace"
    - "an optimal algorithm for the separating common tangents of two polygons"

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)