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: "symmetric randomized dependent rounding"
  Abstract: Dependent rounding is a popular technique in designing approximation algorithms. It allows us to randomly round a fractional solution $x$ to an integral vector $X$ such that $E[X] = x$, all $X_i$'s are ("cylindrically") negatively correlated, and the cardinality constraint $\sum_i X_i = \sum_i x_i$ holds with probability one. One can also essentially replace the cardinality constraint by a general linear constraint $\sum_i a_i X_i = \sum_i a_i x_i$ if the $a_i$'s are non-negative. However, in certain problems, the coefficients $a_i$ may have mixed signs; furthermore, some applications require correlation inequalities that are much more general than negative correlation. #R##N#In this paper, we propose a generalized dependent rounding technique, called symmetric randomized dependent rounding, which can round $x$ to an almost-integer vector so that any given linear constraint is satisfied with probability one and such that all the variables are nearly independent. We give two illustrative examples of this technique: a randomized algorithm for the knapsack center problem with new average approximation guarantees and an improved bi-criteria approximation ratio for the knapsack median problem.

Connected through hyperedge HE-1 (induced by source paper "a lottery model for center type problems with outliers"):
  with members:
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "the non uniform k center problem"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "matroid and knapsack center problems"
    - "improved approximation algorithms for matroid and knapsack median problems and applications"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-2 (induced by source paper "constant factor approximation for ordered k median"):
  with members:
    - "interpolating between k median and k center approximation algorithms for ordered k median"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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)