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: "constant approximation for k median and k means with outliers via iterative rounding"
  Abstract: In this paper, we present a novel iterative rounding framework for many clustering problems. This leads to an $(\alpha_1 + \epsilon \approx 7.08 + \epsilon)$-approximation algorithm for $k$-median with outliers, greatly improving upon the large implicit constant approximation ratio of Chen [Chen, SODA 08]. For $k$-means with outliers, we give an $(\alpha_2+\epsilon \approx 53 + \epsilon)$-approximation, which is the first $O(1)$-approximation for this problem. The iterative algorithm framework is very versatile; we show how it can be used to give $\alpha_1$- and $(\alpha_1 + \epsilon)$-approximation algorithms for matroid median and knapsack median problems respectively, improving upon the previous best approximations ratios of $8$~[Swamy, ACM Trans.\ Algorithms] and $17.46$ [Byrka et al, ESA 2015]. #R##N#The natural LP relaxation for the $k$-median/$k$-means with outliers problem has an unbounded integrality gap. In spite of this negative result, our iterative rounding framework shows that we can round an LP solution to an \emph{almost-integral} solution of small cost, in which we have at most two fractionally open facilities. Thus, the LP integrality gap arises due to the gap between almost-integral and fully-integral solutions. Then, using a pre-processing procedure, we show how to convert an almost-integral solution to a fully-integral solution losing only a constant-factor in the approximation ratio. By further using a sparsification technique, the additive factor loss incurred by the conversion can be reduced to any $\epsilon > 0$.

Connected through hyperedge HE-1 (induced by source paper "layered sampling for robust optimization problems"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "iterative least trimmed squares for mixed linear regression"
    - "a practical algorithm for distributed clustering and outlier detection"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "efficient algorithms for outlier robust regression"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "sobol tensor trains for global sensitivity analysis"

Connected through hyperedge HE-2 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints"):
  with members:
    - "on a generalization of iterated and randomized rounding"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximation friendly discrepancy rounding"
    - "matroid and knapsack center problems"
    - "approximating k median via pseudo approximation"
    - "fault tolerant facility location a randomized dependent lp rounding algorithm"

Connected through hyperedge HE-3 (induced by source paper "fast noise removal for k means clustering"):
  with members:
    - "a practical algorithm for distributed clustering and outlier detection"
    - "distributed partial clustering"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (induced by source paper "robust federated learning in a heterogeneous environment"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "byzantine stochastic gradient descent"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "federated learning with non iid data"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "learning mixtures of sparse linear regressions using sparse graph codes"

Connected through hyperedge HE-5 (induced by source paper "a practical framework for solving center based clustering with outliers"):
  with members:
    - "beyond worst case analysis"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "distributed partial clustering"
    - "approximation schemes for clustering with outliers"
    - "the non uniform k center problem"
    - "approximating k median via pseudo approximation"
    - "optimal time bounds for approximate clustering"

Connected through hyperedge HE-6 (induced by source paper "a constant approximation for colorful k center"):
  with members:
    - "on the partition set cover problem"
    - "approximation schemes for clustering with outliers"
    - "matroid and knapsack center problems"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-7 (induced by source paper "near linear time approximation schemes for clustering in doubling metrics"):
  with members:
    - "travelling on graphs with small highway dimension"
    - "the parameterized hardness of the k center problem in transportation networks"
    - "approximation schemes for capacitated clustering in doubling metrics"
    - "a fast approximation scheme for low dimensional k means"
    - "approximation schemes for clustering with outliers"
    - "a ptas for the steiner forest problem in doubling metrics"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"

Connected through hyperedge HE-8 (induced by source paper "low rank approximation in the presence of outliers"):
  with members:
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "fast algorithms for robust pca via gradient descent"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"

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)