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: "approximate hierarchical clustering via sparsest cut and spreading metrics"
  Abstract: Dasgupta recently introduced a cost function for the hierarchical clustering of a set of points given pairwise similarities between them. He showed that this function is NP-hard to optimize, but a top-down recursive partitioning heuristic based on an alpha_n-approximation algorithm for uniform sparsest cut gives an approximation of O(alpha_n log n) (the current best algorithm has alpha_n=O(sqrt{log n})). We show that the aforementioned sparsest cut heuristic in fact obtains an O(alpha_n)-approximation for hierarchical clustering. The algorithm also applies to a generalized cost function studied by Dasgupta. Moreover, we obtain a strong inapproximability result, showing that the hierarchical clustering objective is hard to approximate to within any constant factor assuming the Small-Set Expansion (SSE) Hypothesis. Finally, we discuss approximation algorithms based on convex relaxations. We present a spreading metric SDP relaxation for the problem and show that it has integrality gap at most O(sqrt{log n}). The advantage of the SDP relative to the sparsest cut heuristic is that it provides an explicit lower bound on the optimal solution and could potentially yield an even better approximation for hierarchical clustering. In fact our analysis of this SDP served as the inspiration for our improved analysis of the sparsest cut heuristic. We also show that a spreading metric LP relaxation gives an O(log n)-approximation.

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

Connected through hyperedge HE-3 (induced by source paper "analysis of ward s method"):
  with members:
    - "metric perturbation resilience"
    - "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"
    - "computational feasibility of clustering under clusterability assumptions"
    - "the hardness of approximation of euclidean k means"
    - "analysis of agglomerative clustering"
    - "robust hierarchical clustering"

Connected through hyperedge HE-4 (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"
    - "hierarchical clustering via spreading metrics"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-5 (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"
    - "hierarchical clustering via spreading metrics"

Connected through hyperedge HE-6 (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"
    - "hierarchical clustering via spreading metrics"
    - "a cost function for similarity based hierarchical clustering"

Connected through hyperedge HE-7 (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"
    - "hierarchical clustering via spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-8 (induced by source paper "reliable agglomerative clustering"):
  with members:
    - "hierarchical clustering via spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "robust hierarchical clustering"
    - "a tutorial on spectral 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)