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: "revisiting k means new algorithms via bayesian nonparametrics"
  Abstract: Bayesian models offer great flexibility for clustering applications---Bayesian nonparametrics can be used for modeling infinite mixtures, and hierarchical Bayesian models can be utilized for sharing clusters across multiple data sets. For the most part, such flexibility is lacking in classical clustering methods such as k-means. In this paper, we revisit the k-means clustering algorithm from a Bayesian nonparametric viewpoint. Inspired by the asymptotic connection between k-means and mixtures of Gaussians, we show that a Gibbs sampling algorithm for the Dirichlet process mixture approaches a hard clustering algorithm in the limit, and further that the resulting algorithm monotonically minimizes an elegant underlying k-means-like clustering objective that includes a penalty for the number of clusters. We generalize this analysis to the case of clustering multiple data sets through a similar asymptotic argument with the hierarchical Dirichlet process. We also discuss further extensions that highlight the benefits of our analysis: i) a spectral relaxation involving thresholded eigenvectors, and ii) a normalized cut graph clustering algorithm that does not fix the number of clusters in the graph.

Connected through hyperedge HE-1 (induced by source paper "infinite mixture prototypes for few shot learning"):
  with members:
    - "on first order meta learning algorithms"
    - "prototypical networks for few shot learning"
    - "learning to remember rare events"
    - "matching networks for one shot learning"
    - "facenet a unified embedding for face recognition and clustering"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "penalized k means algorithms for finding the correct number of clusters in a dataset"):
  with members:
    - "recombinator k means enhancing k means by seeding from pools of previous runs"

Connected through hyperedge HE-3 (induced by source paper "generalized dirichlet process means for f separable distortion measures"):
  with members:
    - "optimistic concurrency control for distributed unsupervised learning"

Connected through hyperedge HE-4 (induced by source paper "contingency aware exploration in reinforcement learning"):
  with members:
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "disentangling controllable and uncontrollable factors of variation by interacting with the world"
    - "on learning intrinsic rewards for policy gradient methods"
    - "diversity is all you need learning skills without a reward function"
    - "learning to navigate in cities without a map"
    - "parameter space noise for exploration"
    - "independently controllable features"

Connected through hyperedge HE-5 (induced by source paper "deep multimodal clustering for unsupervised audiovisual learning"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "a report on sound event detection with different binaural features"
    - "learning word like units from joint audio visual analysis"
    - "objects that sound"

Connected through hyperedge HE-6 (induced by source paper "the alignment of the spheres globally optimal spherical mixture alignment for camera pose estimation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "geometric loss functions for camera pose regression with deep learning"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"

Connected through hyperedge HE-7 (induced by source paper "generalizing robot imitation learning with invariant hidden semi markov models"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "transform invariant non parametric clustering of covariance matrices and its application to unsupervised joint segmentation and action discovery"
    - "ddco discovery of deep continuous options for robot learning from demonstrations"
    - "multi level discovery of deep options"
    - "one shot imitation learning"
    - "generative adversarial imitation learning"
    - "small variance asymptotics for non parametric online robot learning"

Connected through hyperedge HE-8 (induced by source paper "spf celltracker tracking multiple cells with strongly correlated moves using a spatial particle filter"):
  with members:
    - "visualization and correction of automated segmentation tracking and lineaging from 5 d stem cell image sequences"

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)