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: "approximation schemes for clustering with outliers"
  Abstract: Clustering problems are well-studied in a variety of fields such as data science, operations research, and computer science. Such problems include variants of centre location problems, $k$-median, and $k$-means to name a few. In some cases, not all data points need to be clustered; some may be discarded for various reasons. #R##N#We study clustering problems with outliers. More specifically, we look at Uncapacitated Facility Location (UFL), $k$-Median, and $k$-Means. In UFL with outliers, we have to open some centres, discard up to $z$ points of $\cal X$ and assign every other point to the nearest open centre, minimizing the total assignment cost plus centre opening costs. In $k$-Median and $k$-Means, we have to open up to $k$ centres but there are no opening costs. In $k$-Means, the cost of assigning $j$ to $i$ is $\delta^2(j,i)$. We present several results. Our main focus is on cases where $\delta$ is a doubling metric or is the shortest path metrics of graphs from a minor-closed family of graphs. For uniform-cost UFL with outliers on such metrics we show that a multiswap simple local search heuristic yields a PTAS. With a bit more work, we extend this to bicriteria approximations for the $k$-Median and $k$-Means problems in the same metrics where, for any constant $\epsilon > 0$, we can find a solution using $(1+\epsilon)k$ centres whose cost is at most a $(1+\epsilon)$-factor of the optimum and uses at most $z$ outliers. We also show that natural local search heuristics that do not violate the number of clusters and outliers for $k$-Median (or $k$-Means) will have unbounded gap even in Euclidean metrics. Furthermore, we show how our analysis can be extended to general metrics for $k$-Means with outliers to obtain a $(25+\epsilon,1+\epsilon)$ bicriteria.

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 "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"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "the non uniform k center problem"
    - "approximating k median via pseudo approximation"
    - "optimal time bounds for approximate clustering"

Connected through hyperedge HE-3 (induced by source paper "a constant approximation for colorful k center"):
  with members:
    - "on the partition set cover problem"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "matroid and knapsack center problems"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-4 (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"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "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-5 (induced by source paper "on euclidean k means clustering with alpha center proximity"):
  with members:
    - "exact algorithms and lower bounds for stable instances of euclidean k means"
    - "clustering stable instances of euclidean k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "k center clustering under perturbation resilience"
    - "computational feasibility of clustering under clusterability assumptions"
    - "faster algorithms for the constrained k means problem"

Connected through hyperedge HE-6 (induced by source paper "an o log 1 5 n log log n approximation algorithm for mean isoperimetry and robust k means"):
  with members:
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "the power of local search for clustering"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-7 (induced by source paper "constant approximation for k median and k means with outliers via iterative rounding"):
  with members:
    - "the power of local search for clustering"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "improved and simplified inapproximability for k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "improved approximation algorithms for matroid and knapsack median problems and applications"
    - "approximating k median via pseudo approximation"
    - "clustering with spectral norm and the k means algorithm"

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)