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: "a lottery model for center type problems with outliers"
  Abstract: In this paper, we give tight approximation algorithms for the $k$-center and matroid center problems with outliers. Unfairness arises naturally in this setting: certain clients could always be considered as outliers. To address this issue, we introduce a lottery model in which each client $j$ is allowed to submit a parameter $p_j \in [0,1]$ and we look for a random solution that covers every client $j$ with probability at least $p_j$. Our techniques include a randomized rounding procedure to round a point inside a matroid intersection polytope to a basis plus at most one extra item such that all marginal probabilities are preserved and such that a certain linear function of the variables does not decrease in the process with probability one.

Connected through hyperedge HE-1 (induced by source paper "coreset based strategies for robust center type problems"):
  with members:
    - "a general coreset based approach to diversity maximization under matroid constraints"
    - "small space stream summary for matroid center"
    - "generalized center problems with outliers"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "matroid and knapsack center problems"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-2 (induced by source paper "approximation algorithms for stochastic clustering"):
  with members:
    - "fair clustering through fairlets"
    - "improvements on the k center problem for uncertain data"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "stochastic k center and j flat center problems"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"

Connected through hyperedge HE-3 (induced by source paper "generalized center problems with outliers"):
  with members:
    - "the heterogeneous capacitated k center problem"
    - "the non uniform k center problem"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "lp based algorithms for capacitated facility location"
    - "approximating capacitated k median with 1 epsilon k open facilities"
    - "matroid and knapsack center problems"

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)