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: "interactive bayesian hierarchical clustering"
  Abstract: Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user's needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering while still utilizing the geometry of the data by sampling a constrained posterior distribution over hierarchies. We also suggest several ways to intelligently query a user. The algorithm, along with the querying schemes, shows promising results on real data.

Connected through hyperedge HE-1 (induced by source paper "diameter based interactive structure discovery"):
  with members:
    - "diameter based active learning"
    - "a general framework for robust interactive learning"
    - "equality of opportunity in supervised learning"
    - "clustering with same cluster queries"
    - "efficient learning of linear separators under bounded noise"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "local algorithms for interactive clustering"
    - "robust interactive learning"

Connected through hyperedge HE-2 (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"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

Connected through hyperedge HE-3 (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"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"

Connected through hyperedge HE-4 (induced by source paper "synthesizing queries via interactive sketching"):
  with members:
    - "selecting representative examples for program synthesis"
    - "active learning of points to specifications"
    - "program synthesis using conflict driven learning"
    - "interactive program synthesis"
    - "deepcoder learning to write programs"
    - "synthesizing program input grammars"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"

Connected through hyperedge HE-5 (induced by source paper "diameter based interactive structure search"):
  with members:
    - "a general framework for robust interactive learning"
    - "clustering with same cluster queries"
    - "efficient learning of linear separators under bounded noise"
    - "local algorithms for interactive clustering"

Connected through hyperedge HE-6 (induced by source paper "active classification with comparison queries"):
  with members:
    - "dimension coupling optimal active learning of halfspaces via query synthesis"
    - "clustering with same cluster queries"
    - "the optimal sample complexity of pac learning"
    - "beyond disagreement based agnostic active learning"
    - "a compression technique for analyzing disagreement based active learning"
    - "minimax analysis of active learning"
    - "learning using local membership queries"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-7 (induced by source paper "adaptive hierarchical clustering using ordinal queries"):
  with members:
    - "a cost function for similarity based hierarchical clustering"
    - "deterministic and probabilistic binary search in graphs"
    - "local algorithms for interactive clustering"
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "learning multi modal similarity"
    - "noisy sorting without resampling"

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)