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: "diversity promoting bayesian learning of latent variable models"
  Abstract: To address three important issues involved in latent variable models (LVMs), including capturing infrequent patterns, achieving small-sized but expressive models and alleviating overfitting, several studies have been devoted to "diversifying" LVMs, which aim at encouraging the components in LVMs to be diverse. Most existing studies fall into a frequentist-style regularization framework, where the components are learned via point estimation. In this paper, we investigate how to "diversify" LVMs in the paradigm of Bayesian learning. We propose two approaches that have complementary advantages. One is to define a diversity-promoting mutual angular prior which assigns larger density to components with larger mutual angles and use this prior to affect the posterior via Bayes' rule. We develop two efficient approximate posterior inference algorithms based on variational inference and MCMC sampling. The other approach is to impose diversity-promoting regularization directly over the post-data distribution of components. We also extend our approach to "diversify" Bayesian nonparametric models where the number of components is infinite. A sampling algorithm based on slice sampling and Hamiltonian Monte Carlo is developed. We apply these methods to "diversify" Bayesian mixture of experts model and infinite latent feature model. Experiments on various datasets demonstrate the effectiveness and efficiency of our methods.

Connected through hyperedge HE-1 (induced by source paper "compressive hyperspherical energy minimization"):
  with members:
    - "decoupled networks"
    - "group normalization"
    - "learning towards minimum hyperspherical energy"
    - "darts differentiable architecture search"
    - "deep semi random features for nonlinear function approximation"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"

Connected through hyperedge HE-2 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "tagging like humans diverse and distinct image annotation"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "learning detection with diverse proposals"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"
    - "mutual information and diverse decoding improve neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "improving singing voice separation with the wave u net using minimum hyperspherical energy"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "learning towards minimum hyperspherical energy"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "learning towards minimum hyperspherical energy"):
  with members:
    - "decoupled networks"
    - "orthogonality promoting distance metric learning convex relaxation and theoretical analysis"
    - "the devil of face recognition is in the noise"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "spectral normalization for generative adversarial networks"
    - "additive margin softmax for face verification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "latent topic conversational models"):
  with members:
    - "latent intention dialogue models"
    - "referenceless quality estimation for natural language generation"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "latent variable dialogue models and their diversity"
    - "steering output style and topic in neural response generation"
    - "discovering discrete latent topics with neural variational inference"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"

Connected through hyperedge HE-6 (induced by source paper "selective harvesting over networks"):
  with members:
    - "analyzing the optimal neighborhood algorithms for budgeted and partial connected dominating set problems"
    - "building bridges viewing active learning from the multi armed bandit lens"
    - "the power of local information in social networks"
    - "bayesian optimal active search and surveying"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "importance weighted active learning"

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)