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: "data stability in clustering a closer look"
  Abstract: We consider the model introduced by Bilu and Linial (2010), who study problems for which the optimal clustering does not change when distances are perturbed. They show that even when a problem is NP-hard, it is sometimes possible to obtain efficient algorithms for instances resilient to certain multiplicative perturbations, e.g. on the order of $O(\sqrt{n})$ for max-cut clustering. Awasthi et al. (2010) consider center-based objectives, and Balcan and Liang (2011) analyze the $k$-median and min-sum objectives, giving efficient algorithms for instances resilient to certain constant multiplicative perturbations. #R##N#Here, we are motivated by the question of to what extent these assumptions can be relaxed while allowing for efficient algorithms. We show there is little room to improve these results by giving NP-hardness lower bounds for both the $k$-median and min-sum objectives. On the other hand, we show that constant multiplicative resilience parameters can be so strong as to make the clustering problem trivial, leaving only a narrow range of resilience parameters for which clustering is interesting. We also consider a model of additive perturbations and give a correspondence between additive and multiplicative notions of stability. Our results provide a close examination of the consequences of assuming stability in data.

Connected through hyperedge HE-1 (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"
    - "approximation schemes for clustering with outliers"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "one size fits all effectiveness of local search on structured data"):
  with members:
    - "the power of local search for clustering"
    - "tight analysis of a multiple swap heuristic for budgeted red blue median"
    - "local search yields a ptas for k means in doubling metrics"
    - "on variants of k means clustering"
    - "k center clustering under perturbation resilience"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-3 (induced by source paper "when is clustering perturbation robust"):
  with members:
    - "computational feasibility of clustering under clusterability assumptions"
    - "incremental clustering the case for extra clusters"
    - "a uniqueness theorem for clustering"
    - "center based clustering under perturbation stability"
    - "are stable instances easy"

Connected through hyperedge HE-4 (induced by source paper "clustering with same cluster queries"):
  with members:
    - "computational feasibility of clustering under clusterability assumptions"
    - "center based clustering under perturbation stability"

Connected through hyperedge HE-5 (induced by source paper "computational feasibility of clustering under clusterability assumptions"):
  with members:
    - "on the practically interesting instances of maxcut"
    - "center based clustering under perturbation stability"
    - "are stable instances easy"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-6 (induced by source paper "symmetric and asymmetric k center clustering under stability"):
  with members:
    - "decompositions of triangle dense graphs"
    - "beyond worst case analysis in private singular vector computation"
    - "the complexity of planar counting problems"

Connected through hyperedge HE-7 (induced by source paper "on coloring resilient graphs"):
  with members:
    - "improved hardness of approximating chromatic number"
    - "anti coordination games and stable graph colorings"
    - "a characterization of b perfect graphs"
    - "center based clustering under perturbation stability"
    - "are stable instances easy"
    - "conditional hardness for approximate coloring"
    - "algorithms for coloring quadtrees"

Connected through hyperedge HE-8 (induced by source paper "bilu linial stable instances of max cut and minimum multiway cut"):
  with members:
    - "on the practically interesting instances of maxcut"
    - "clustering under perturbation resilience"
    - "center based clustering under perturbation stability"
    - "parallel repetition simplification and the no signaling case"
    - "a log n omega 1 integrality gap for the sparsest cut sdp"
    - "are stable instances easy"

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.LG(Machine Learning)