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: "hierarchical clustering better than average linkage"
  Abstract: Hierarchical Clustering (HC) is a widely studied problem in exploratory data analysis, usually tackled by simple agglomerative procedures like average-linkage, single-linkage or complete-linkage. In this paper we focus on two objectives, introduced recently to give insight into the performance of average-linkage clustering: a similarity based HC objective proposed by [Moseley and Wang, 2017] and a dissimilarity based HC objective proposed by [Cohen-Addad et al., 2018]. In both cases, we present tight counterexamples showing that average-linkage cannot obtain better than 1/3 and 2/3 approximations respectively (in the worst-case), settling an open question raised in [Moseley and Wang, 2017]. This matches the approximation ratio of a random solution, raising a natural question: can we beat average-linkage for these objectives? We answer this in the affirmative, giving two new algorithms based on semidefinite programming with provably better guarantees.

Connected through hyperedge HE-1 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection"):
  with members:
    - "hierarchical clustering for euclidean data"
    - "hierarchical clustering with structural constraints"
    - "adaptive hierarchical clustering using ordinal queries"
    - "hierarchical clustering objective functions and algorithms"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "interactive bayesian hierarchical clustering"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

Connected through hyperedge HE-2 (induced by source paper "supervised hierarchical clustering with exponential linkage"):
  with members:
    - "hierarchical clustering with structural constraints"
    - "resolving event coreference with supervised representation learning and clustering oriented regularization"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-3 (induced by source paper "hierarchical clustering objective functions and algorithms"):
  with members:
    - "hierarchical clustering with structural constraints"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "on the practically interesting instances of maxcut"
    - "a uniqueness theorem for clustering"
    - "improved spectral norm bounds for clustering"
    - "efficient active algorithms for hierarchical clustering"

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)