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: "accurate uncertainties for deep learning using calibrated regression"
  Abstract: Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate -- for example, a 90% credible interval may not contain the true outcome 90% of the time. Here, we propose a simple procedure for calibrating any regression algorithm; when applied to Bayesian and probabilistic models, it is guaranteed to produce calibrated uncertainty estimates given enough data. Our procedure is inspired by Platt scaling and extends previous work on classification. We evaluate this approach on Bayesian linear regression, feedforward, and recurrent neural networks, and find that it consistently outputs well-calibrated credible intervals while improving performance on time series forecasting and model-based reinforcement learning tasks.

Connected through hyperedge HE-1 (induced by source paper "a financial service chatbot based on deep bidirectional transformers"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "selectivenet a deep neural network with an integrated reject option"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "an evaluation dataset for intent classification and out of scope prediction"
    - "roberta a robustly optimized bert pretraining approach"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "sampling based bayesian inference with gradient uncertainty"

Connected through hyperedge HE-2 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "efficient uncertainty estimation for semantic segmentation in videos"

Connected through hyperedge HE-3 (induced by source paper "learnable bernoulli dropout for bayesian deep learning"):
  with members:
    - "adaptive activity monitoring with uncertainty quantification in switching gaussian process models"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "vae with a vampprior"
    - "understanding deep learning requires rethinking generalization"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-4 (induced by source paper "calibrated prediction with covariate shift via unsupervised domain adaptation"):
  with members:
    - "pac confidence sets for deep neural networks via calibrated prediction"
    - "on calibration of modern neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "conditional adversarial domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "estimating uncertainty online against an adversary"
    - "conditional validity of inductive conformal predictors"

Connected through hyperedge HE-5 (induced by source paper "quantile regularization towards implicit calibration of regression models"):
  with members:
    - "verified uncertainty calibration"
    - "evaluating model calibration in classification"
    - "when does label smoothing help"
    - "on calibration of modern neural networks"
    - "mixup beyond empirical risk minimization"
    - "regularizing neural networks by penalizing confident output distributions"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes"):
  with members:
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "deep rigid instance scene flow"
    - "lightweight probabilistic deep networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-7 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "neural processes"
    - "are generative deep models for novelty detection truly better"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "novelty detection with gan"

Connected through hyperedge HE-8 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  with members:
    - "using pre training can improve model robustness and uncertainty"
    - "lightweight probabilistic deep networks"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "arcface additive angular margin loss for deep face recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "a less biased evaluation of out of distribution sample detectors"

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)