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 via spreading metrics"
  Abstract: We study the cost function for hierarchical clusterings introduced by [arXiv:1510.05043] where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in [arXiv:1510.05043] that a top-down algorithm returns a hierarchical clustering of cost at most $O\left(\alpha_n \log n\right)$ times the cost of the optimal hierarchical clustering, where $\alpha_n$ is the approximation ratio of the Sparsest Cut subroutine used. Thus using the best known approximation algorithm for Sparsest Cut due to Arora-Rao-Vazirani, the top down algorithm returns a hierarchical clustering of cost at most $O\left(\log^{3/2} n\right)$ times the cost of the optimal solution. We improve this by giving an $O(\log{n})$-approximation algorithm for this problem. Our main technical ingredients are a combinatorial characterization of ultrametrics induced by this cost function, deriving an Integer Linear Programming (ILP) formulation for this family of ultrametrics, and showing how to iteratively round an LP relaxation of this formulation by using the idea of \emph{sphere growing} which has been extensively used in the context of graph partitioning. We also prove that our algorithm returns an $O(\log{n})$-approximate hierarchical clustering for a generalization of this cost function also studied in [arXiv:1510.05043]. Experiments show that the hierarchies found by using the ILP formulation as well as our rounding algorithm often have better projections into flat clusters than the standard linkage based algorithms. We also give constant factor inapproximability results for this problem.

Connected through hyperedge HE-1 (induced by source paper "hierarchical correlation clustering and tree preserving embedding"):
  with members:
    - "correlation clustering with adaptive similarity queries"
    - "correlation clustering with low rank matrices"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "robust hierarchical clustering"

Connected through hyperedge HE-2 (induced by source paper "compact representation of uncertainty in hierarchical clustering"):
  with members:
    - "hierarchical clustering objective functions and algorithms"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"

Connected through hyperedge HE-3 (induced by source paper "efficient parameter free clustering using first neighbor relations"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "deep clustering for unsupervised learning of visual features"
    - "multi view low rank sparse subspace clustering"
    - "deep continuous clustering"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "clustering millions of faces by identity"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-4 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "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"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

Connected through hyperedge HE-5 (induced by source paper "ultrametric fitting by gradient descent"):
  with members:
    - "on the convergence of adam and beyond"
    - "reliable agglomerative clustering"
    - "hierarchical graph clustering using node pair sampling"
    - "hierarchical clustering with structural constraints"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "interactive bayesian hierarchical clustering"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"

Connected through hyperedge HE-6 (induced by source paper "supervised hierarchical clustering with exponential linkage"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "hierarchical clustering with structural constraints"
    - "resolving event coreference with supervised representation learning and clustering oriented regularization"
    - "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-7 (induced by source paper "reliable agglomerative clustering"):
  with members:
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "robust hierarchical clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "hierarchical clustering with structural constraints"):
  with members:
    - "robust hierarchical clustering"
    - "local algorithms for interactive 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.LG(Machine Learning)