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: "robust hierarchical clustering"
  Abstract: One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret. Unfortunately, it is well known, however, that many of the classic agglomerative clustering algorithms are not robust to noise. In this paper we propose and analyze a new robust algorithm for bottom-up agglomerative clustering. We show that our algorithm can be used to cluster accurately in cases where the data satisfies a number of natural properties and where the traditional agglomerative algorithms fail. We also show how to adapt our algorithm to the inductive setting where our given data is only a small random sample of the entire data set. Experimental evaluations on synthetic and real world data sets show that our algorithm achieves better performance than other hierarchical algorithms in the presence of noise.

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"
    - "a cost function for similarity based hierarchical clustering"

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"
    - "computational feasibility of clustering under clusterability assumptions"
    - "the hardness of approximation of euclidean k means"
    - "analysis of agglomerative clustering"

Connected through hyperedge HE-3 (induced by source paper "clustering degree corrected stochastic block model with outliers"):
  with members:
    - "community detection thresholds and the weak ramanujan property"
    - "clustering partially observed graphs via convex optimization"
    - "nuclear norm minimization for the planted clique and biclique problems"

Connected through hyperedge HE-4 (induced by source paper "reliable agglomerative clustering"):
  with members:
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "hierarchical clustering with structural constraints"):
  with members:
    - "hierarchical clustering via spreading metrics"
    - "local algorithms for interactive clustering"

Connected through hyperedge HE-6 (induced by source paper "independence clustering without a matrix"):
  with members:
    - "kernel based conditional independence test and application in causal discovery"
    - "nonparametric statistical inference for ergodic processes"
    - "universal codes as a basis for time series testing"

Connected through hyperedge HE-7 (induced by source paper "approximate hierarchical clustering via sparsest cut and spreading metrics"):
  with members:
    - "a cost function for similarity based hierarchical clustering"
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "reductions between expansion problems"

Connected through hyperedge HE-8 (induced by source paper "improving quality of hierarchical clustering for large data series"):
  with members:
    - "one billion word benchmark for measuring progress in statistical language modeling"

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)