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: "perturbation resilient clustering for k center and related problems via lp relaxations"
  Abstract: We consider clustering in the perturbation resilience model that has been studied since the work of Bilu and Linial [ICS, 2010] and Awasthi, Blum and Sheffet [Inf. Proc. Lett., 2012]. A clustering instance $I$ is said to be $\alpha$-perturbation resilient if the optimal solution does not change when the pairwise distances are modified by a factor of $\alpha$ and the perturbed distances satisfy the metric property --- this is the metric perturbation resilience property introduced in Angelidakis et. al. [STOC, 2010] and a weaker requirement than prior models. We make two high-level contributions. #R##N#1) We show that the natural LP relaxation of $k$-center and asymmetric $k$-center is integral for $2$-perturbation resilient instances. We belive that demonstrating the goodness of standard LP relaxations complements existing results that are based on combinatorial algorithms designed for the perturbation model. #R##N#2) We define a simple new model of perturbation resilience for clustering with \emph{outliers}. Using this model we show that the unified MST and dynamic programming based algorithm proposed by Angelidakis et. al. [STOC, 2010] exactly solves the clustering with outliers problem for several common center based objectives (like $k$-center, $k$-means, $k$-median) when the instances is $2$-perturbation resilient. We further show that a natural LP relxation is integral for $2$-perturbation resilient instances of \kcenter with outliers.

Connected through hyperedge HE-1 (induced by source paper "bilu linial stability certified algorithms and the independent set problem"):
  with members:
    - "on euclidean k means clustering with alpha center proximity"
    - "exact algorithms and lower bounds for stable instances of euclidean k means"
    - "stability and recovery for independence systems"
    - "lp based robust algorithms for noisy minor free and bounded treewidth graphs"
    - "k center clustering under perturbation resilience"
    - "on the lov asz theta function for independent sets in sparse graphs"
    - "sum of squares lower bounds for planted clique"
    - "bilu linial stable instances of max cut and minimum multiway cut"

Connected through hyperedge HE-2 (induced by source paper "robust communication optimal distributed clustering algorithms"):
  with members:
    - "distributed partial clustering"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "communication optimal distributed clustering"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "data driven resource allocation for distributed learning"
    - "k center clustering under perturbation resilience"
    - "a bi criteria approximation algorithm for k means"

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)