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: "bayesian inference with posterior regularization and applications to infinite latent svms"
  Abstract: Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes' rule, imposing posterior regularization is arguably more direct and in some cases more natural and general. In this paper, we present regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired post-data posterior distribution under an information theoretical formulation. RegBayes is more flexible than the procedure that elicits expert knowledge via priors, and it covers both directed Bayesian networks and undirected Markov networks whose Bayesian formulation results in hybrid chain graph models. When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes. Furthermore, we present two concrete examples of RegBayes, infinite latent support vector machines (iLSVM) and multi-task infinite latent support vector machines (MT-iLSVM), which explore the large-margin idea in combination with a nonparametric Bayesian model for discovering predictive latent features for classification and multi-task learning, respectively. We present efficient inference methods and report empirical studies on several benchmark datasets, which appear to demonstrate the merits inherited from both large-margin learning and Bayesian nonparametrics. Such results were not available until now, and contribute to push forward the interface between these two important subfields, which have been largely treated as isolated in the community.

Connected through hyperedge HE-1 (induced by source paper "avgout a simple output probability measure to eliminate dull responses"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "improving abstraction in text summarization"
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "polite dialogue generation without parallel data"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "learning to decode for future success"
    - "generating high quality and informative conversation responses with sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "on implicit regularization in beta vaes"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "from variational to deterministic autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "latent space non linear statistics"
    - "explaining explanations an overview of interpretability of machine learning"
    - "variational autoencoders pursue pca directions by accident"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-3 (induced by source paper "a simple yet effective baseline for robust deep learning with noisy labels"):
  with members:
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "autoaugment learning augmentation policies from data"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-4 (induced by source paper "regularizing generative models using knowledge of feature dependence"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "joint embedding of words and labels for text classification"
    - "mine mutual information neural estimation"
    - "information constraints on auto encoding variational bayes"
    - "knowledge based distant regularization in learning probabilistic models"
    - "regularising non linear models using feature side information"
    - "harnessing deep neural networks with logic rules"
    - "density estimation using real nvp"

Connected through hyperedge HE-5 (induced by source paper "transfer learning between related tasks using expected label proportions"):
  with members:
    - "exploiting document knowledge for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "cross lingual pseudo projected expectation regularization for weakly supervised learning"

Connected through hyperedge HE-6 (induced by source paper "harnessing low fidelity data to accelerate bayesian optimization via posterior regularization"):
  with members:
    - "robust video object tracking via bayesian model averaging based feature fusion"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"

Connected through hyperedge HE-7 (induced by source paper "deep generative models with learnable knowledge constraints"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "learning to write with cooperative discriminators"
    - "unsupervised person image synthesis in arbitrary poses"
    - "training classifiers with natural language explanations"
    - "toward controlled generation of text"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-8 (induced by source paper "generating more interesting responses in neural conversation models with distributional constraints"):
  with members:
    - "discourse aware neural rewards for coherent text generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"
    - "quac question answering in context"
    - "learning conversational systems that interleave task and non task content"
    - "opennmt open source toolkit for neural machine translation"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "towards an automatic turing test learning to evaluate dialogue responses"

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)