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: "improved balanced flow computation using parametric flow"
  Abstract: We present a new algorithm for computing balanced flows in equality networks arising in market equilibrium computations. The current best time bound for computing balanced flows in such networks requires O ( n ) maxflow computations, where n is the number of nodes in the network 1. Our algorithm requires only a single parametric flow computation. The best algorithm for computing parametric flows 3 is only by a logarithmic factor slower than the best algorithms for computing maxflows. Hence, the running time of the algorithms in Devanur et al. 1 and Duan and Mehlhorn 2 for computing market equilibria in linear Fisher and Arrow-Debreu markets improve by almost a factor of n. Balanced flows are an important ingredient in market equilibrium computations.An improved algorithm for computing balanced flows in equality networks is presented.The algorithm is an application of parametric flows.The algorithm improves the running time of some market equilibrium algorithms.

Connected through hyperedge HE-1 (induced by source paper "a strongly polynomial algorithm for linear exchange markets"):
  with members:
    - "a simpler and faster strongly polynomial algorithm for generalized flow maximization"
    - "an improved combinatorial polynomial algorithm for the linear arrow debreu market"
    - "a strongly polynomial algorithm for generalized flow maximization"
    - "a rational convex program for linear arrow debreu markets"
    - "a combinatorial polynomial algorithm for the linear arrow debreu market"
    - "concave generalized flows with applications to market equilibria"

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)