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: "beyond worst case analysis"
  Abstract: In the worst-case analysis of algorithms, the overall performance of an algorithm is summarized by its worst performance on any input. This approach has countless success stories, but there are also important computational problems --- like linear programming, clustering, online caching, and neural network training --- where the worst-case analysis framework does not provide any helpful advice on how to solve the problem. This article covers a number of modeling methods for going beyond worst-case analysis and articulating which inputs are the most relevant.

Connected through hyperedge HE-1 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer"):
  with members:
    - "simple versus optimal contracts"
    - "approximate revenue maximization with multiple items"
    - "learning simple auctions"
    - "the sample complexity of revenue maximization"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "strong duality for a multiple good monopolist"
    - "the complexity of optimal mechanism design"

Connected through hyperedge HE-2 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension"):
  with members:
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "the non uniform k center problem"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"
    - "approximation and streaming algorithms for projective clustering via random projections"

Connected through hyperedge HE-3 (induced by source paper "a framework for robust realistic geometric computations"):
  with members:
    - "chirotopes of random points in space are realizable on a small integer grid"
    - "smoothed analysis of order types"
    - "optimal curve straightening is exists mathbb r complete"
    - "the complexity of drawing a graph in a polygonal region"
    - "on order types of random point sets"
    - "on the complexity of minimum link path problems"
    - "recognition and complexity of point visibility graphs"
    - "convex hull of points lying on lines in o nlogn time after preprocessing"

Connected through hyperedge HE-4 (induced by source paper "minimum enclosing ball revisited stability and sub linear time algorithms"):
  with members:
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "clustering under perturbation resilience"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-5 (induced by source paper "smoothed analysis of order types"):
  with members:
    - "smoothed analysis of the art gallery problem"
    - "the complexity of drawing a graph in a polygonal region"
    - "a friendly smoothed analysis of the simplex method"
    - "intersection graphs of rays and grounded segments"
    - "the complexity of tensor rank"
    - "recognition and complexity of point visibility graphs"
    - "intersection graphs of segments and exists mathbb r"
    - "satisfiability of cross product terms is complete for real nondeterministic polytime blum shub smale machines"

Connected through hyperedge HE-6 (induced by source paper "a practical framework for solving center based clustering with outliers"):
  with members:
    - "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"
    - "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-7 (induced by source paper "scheduling with predictions and the price of misprediction"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "soap one clean analysis of all age based scheduling policies"
    - "submodular optimization under noise"
    - "the limitations of optimization from samples"
    - "satzilla portfolio based algorithm selection for sat"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-8 (induced by source paper "random projections and sampling algorithms for clustering of high dimensional polygonal curves"):
  with members:
    - "on the hardness of computing an average curve"
    - "the one way communication complexity of dynamic time warping distance"
    - "walking the dog fast in practice algorithm engineering of the fr echet distance"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "clustering is difficult only when it does not matter"

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)