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: "posterior calibration and exploratory analysis for natural language processing models"
  Abstract: Many models in natural language processing define probabilistic distributions over linguistic structures. We argue that (1) the quality of a model' s posterior distribution can and should be directly evaluated, as to whether probabilities correspond to empirical frequencies, and (2) NLP uncertainty can be projected not only to pipeline components, but also to exploratory data analysis, telling a user when to trust and not trust the NLP analysis. We present a method to analyze calibration, and apply it to compare the miscalibration of several commonly used models. We also contribute a coreference sampling algorithm that can create confidence intervals for a political event extraction task.

Connected through hyperedge HE-1 (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-2 (induced by source paper "using pre training can improve model robustness and uncertainty"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "logit pairing methods can fool gradient based attacks"
    - "feature denoising for improving adversarial robustness"
    - "dimensionality driven learning with noisy labels"
    - "deep anomaly detection with outlier exposure"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "imbalanced deep learning by minority class incremental rectification"
    - "generalisation in humans and deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep weighted averaging classifiers"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "explainable prediction of medical codes from clinical text"
    - "interpreting neural networks with nearest neighbors"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "a unified approach to interpreting model predictions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "calibration of encoder decoder models for neural machine translation"):
  with members:
    - "accurate uncertainties for deep learning using calibrated regression"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "on calibration of modern neural networks"
    - "on fairness and calibration"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "neural machine translation"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "verified uncertainty calibration"):
  with members:
    - "evaluating model calibration in classification"
    - "measuring calibration in deep learning"
    - "using pre training can improve model robustness and uncertainty"
    - "calibrated model based deep reinforcement learning"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-6 (induced by source paper "deep anomaly detection with outlier exposure"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "an analysis of neural language modeling at multiple scales"
    - "exploring the limits of weakly supervised pretraining"
    - "discriminative out of distribution detection for semantic segmentation"
    - "learning to generate reviews and discovering sentiment"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-7 (induced by source paper "accurate uncertainties for deep learning using calibrated regression"):
  with members:
    - "on calibration of modern neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "estimating uncertainty online against an adversary"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-8 (induced by source paper "rookie a unique approach for exploring news archives"):
  with members:
    - "a neural attention model for abstractive sentence summarization"
    - "on smoothing and inference for topic models"

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.CL(Computation and Language)