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: "approximating the least hypervolume contributor np hard in general but fast in practice"
  Abstract: The hypervolume indicator is an increasingly popular set measure to compare the quality of two Pareto sets. The basic ingredient of most hypervolume indicator based optimization algorithms is the calculation of the hypervolume contribution of single solutions regarding a Pareto set. We show that exact calculation of the hypervolume contribution is #P-hard while its approximation is NP-hard. The same holds for the calculation of the minimal contribution. We also prove that it is NP-hard to decide whether a solution has the least hypervolume contribution. Even deciding whether the contribution of a solution is at most (1+@e) times the minimal contribution is NP-hard. This implies that it is neither possible to efficiently find the least contributing solution (unless P=NP) nor to approximate it (unless NP=BPP). Nevertheless, in the second part of the paper we present a fast approximation algorithm for this problem. We prove that for arbitrarily given @e,@d>0 it calculates a solution with contribution at most (1+@e) times the minimal contribution with probability at least (1-@d). Though it cannot run in polynomial time for all instances, it performs extremely fast on various benchmark datasets. The algorithm solves very large problem instances which are intractable for exact algorithms (e.g., 10,000 solutions in 100 dimensions) within a few seconds.

Connected through hyperedge HE-1 (induced by source paper "learning choice functions"):
  with members:
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "self normalizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"
    - "learning to order things"

Connected through hyperedge HE-2 (induced by source paper "maximum volume subset selection for anchored boxes"):
  with members:
    - "bringing order to special cases of klee s measure problem"
    - "approximation schemes for maximum weight independent set of rectangles"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-3 (induced by source paper "seeding the initial population of multi objective evolutionary algorithms a computational study"):
  with members:
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-4 (induced by source paper "multiplicative approximations optimal hypervolume distributions and the choice of the reference point"):
  with members:
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

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)