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: "computational feasibility of clustering under clusterability assumptions"
  Abstract: It is well known that most of the common clustering objectives are NP-hard to optimize. In practice, however, clustering is being routinely carried out. One approach for providing theoretical understanding of this seeming discrepancy is to come up with notions of clusterability that distinguish realistically interesting input data from worst-case data sets. The hope is that there will be clustering algorithms that are provably efficient on such 'clusterable' instances. In other words, hope that "Clustering is difficult only when it does not matter" (CDNM thesis, for short). #R##N#We believe that to some extent this may indeed be the case. This paper provides a survey of recent papers along this line of research and a critical evaluation their results. Our bottom line conclusion is that that CDNM thesis is still far from being formally substantiated. We start by discussing which requirements should be met in order to provide formal support the validity of the CDNM thesis. In particular, we list some implied requirements for notions of clusterability. We then examine existing results in view of those requirements and outline some research challenges and open questions.

Connected through hyperedge HE-1 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness"):
  with members:
    - "guess free maximization of submodular and linear sums"
    - "stability and recovery for independence systems"
    - "constrained submodular maximization beyond 1 e"
    - "stability of influence maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "lazier than lazy greedy"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-2 (induced by source paper "analysis of ward s method"):
  with members:
    - "metric perturbation resilience"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "a cost function for similarity based hierarchical clustering"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"
    - "analysis of agglomerative clustering"
    - "robust hierarchical clustering"

Connected through hyperedge HE-3 (induced by source paper "data ultrametricity and clusterability"):
  with members:
    - "an effective and efficient approach for clusterability evaluation"
    - "clustering is difficult only when it does not matter"

Connected through hyperedge HE-4 (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"
    - "faster algorithms for the constrained k means problem"

Connected through hyperedge HE-5 (induced by source paper "machine learning friendly set version of johnson lindenstrauss lemma"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "compressive spectral clustering"
    - "random sampling of bandlimited signals on graphs"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "low rank approximation and regression in input sparsity time"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-6 (induced by source paper "stability and recovery for independence systems"):
  with members:
    - "k center clustering under perturbation resilience"
    - "the unreasonable success of local search geometric optimization"
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "improved approximation for 3 dimensional matching via bounded pathwidth local search"
    - "large neighborhood local search for the maximum set packing problem"
    - "clustering is difficult only when it does not matter"
    - "on the practically interesting instances of maxcut"
    - "nash equilibria in perturbation resilient games"

Connected through hyperedge HE-7 (induced by source paper "an aposteriorical clusterability criterion for k means and simplicity of clustering"):
  with members:
    - "one size fits all effectiveness of local search on structured data"
    - "on the discrepancy between kleinberg s clustering axioms and k means clustering algorithm behavior"
    - "an effective and efficient approach for clusterability evaluation"
    - "incremental clustering the case for extra clusters"
    - "clustering with spectral norm and the k means algorithm"
    - "center based clustering under perturbation stability"
    - "are stable instances easy"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "when is clustering perturbation robust"):
  with members:
    - "incremental clustering the case for extra clusters"
    - "a uniqueness theorem for clustering"
    - "data stability in clustering a closer look"
    - "center based clustering under perturbation stability"
    - "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.CC(Computational Complexity)