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: "on calibration of modern neural networks"
  Abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scene graph generation via conditional random fields"

Connected through hyperedge HE-2 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "dropblock a regularization method for convolutional networks"
    - "feature denoising for improving adversarial robustness"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial logit pairing"
    - "autoaugment learning augmentation policies from data"
    - "shakedrop regularization for deep residual learning"

Connected through hyperedge HE-3 (induced by source paper "adversarial robustness for code"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "certified robustness to adversarial word substitutions"
    - "inferring javascript types using graph neural networks"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal transformers"
    - "adversarial attacks on graph neural networks via meta learning"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"

Connected through hyperedge HE-4 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving"):
  with members:
    - "deep evidential regression"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "learning multimodal fixed point weights using gradient descent"
    - "pixor real time 3d object detection from point clouds"
    - "deep active learning for efficient training of a lidar 3d object detector"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "calibration of pre trained transformers"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "calibration of encoder decoder models for neural machine translation"
    - "simplified neural unsupervised domain adaptation"
    - "evaluating lottery tickets under distributional shifts"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "revealing the dark secrets of bert"
    - "how to fine tune bert for text classification"

Connected through hyperedge HE-6 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "the case for bayesian deep learning"
    - "fantastic generalization measures and where to find them"
    - "output constrained bayesian neural networks"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "large scale structure of neural network loss landscapes"
    - "functional variational bayesian neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"

Connected through hyperedge HE-7 (induced by source paper "a comprehensive study on the prediction reliability of graph neural networks for virtual screening"):
  with members:
    - "bayesian deep learning and a probabilistic perspective of generalization"
    - "when does label smoothing help"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "data efficient image recognition with contrastive predictive coding"
    - "self attention graph pooling"
    - "pre training graph neural networks"
    - "dropconnect is effective in modeling uncertainty of bayesian deep networks"
    - "bayesian semi supervised learning for uncertainty calibrated prediction of molecular properties and active learning"

Connected through hyperedge HE-8 (induced by source paper "bayes probe distribution guided sampling for prediction level sets"):
  with members:
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "out of distribution detection in classifiers via generation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "pyro deep universal probabilistic programming"
    - "this looks like that deep learning for interpretable image recognition"
    - "sim to real learning agile locomotion for quadruped robots"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"

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)