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: "a cost function for similarity based hierarchical clustering"
  Abstract: The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction procedure with a provably good approximation ratio.

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"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "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"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

Connected through hyperedge HE-3 (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"
    - "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"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

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"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

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"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-8 (induced by source paper "scalable hierarchical clustering with tree grafting"):
  with members:
    - "improving coreference resolution by learning entity level distributed representations"
    - "learning global features for coreference resolution"
    - "imagenet large scale visual recognition challenge"
    - "efficient active algorithms for hierarchical clustering"
    - "bayesian rose trees"

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)