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: "a tutorial on conformal prediction"
  Abstract: Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability $\epsilon$, together with a method that makes a prediction $\hat{y}$ of a label $y$, it produces a set of labels, typically containing $\hat{y}$, that also contains $y$ with probability $1-\epsilon$. Conformal prediction can be applied to any method for producing $\hat{y}$: a nearest-neighbor method, a support-vector machine, ridge regression, etc. #R##N#Conformal prediction is designed for an on-line setting in which labels are predicted successively, each one being revealed before the next is predicted. The most novel and valuable feature of conformal prediction is that if the successive examples are sampled independently from the same distribution, then the successive predictions will be right $1-\epsilon$ of the time, even though they are based on an accumulating dataset rather than on independent datasets. #R##N#In addition to the model under which successive examples are sampled independently, other on-line compression models can also use conformal prediction. The widely used Gaussian linear model is one of these. #R##N#This tutorial presents a self-contained account of the theory of conformal prediction and works through several numerical examples. A more comprehensive treatment of the topic is provided in "Algorithmic Learning in a Random World", by Vladimir Vovk, Alex Gammerman, and Glenn Shafer (Springer, 2005).

Connected through hyperedge HE-1 (induced by source paper "ml aqp query driven approximate query processing based on machine learning"):
  with members:
    - "approximate query processing using deep generative models"
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "deepdb learn from data not from queries"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "an end to end learning based cost estimator"
    - "estimating cardinalities with deep sketches"
    - "learned cardinalities estimating correlated joins with deep learning"

Connected through hyperedge HE-2 (induced by source paper "trusted confidence bounds for learning enabled cyber physical systems"):
  with members:
    - "improved embeddings with easy positive triplet mining"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "on calibration of modern neural networks"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-3 (induced by source paper "assurance monitoring of cyber physical systems with machine learning components"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "on calibration of modern neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection"):
  with members:
    - "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"
    - "transferable knowledge for low cost decision making in cloud environments"
    - "rectified decision trees towards interpretability compression and empirical soundness"
    - "the university of cambridge s machine translation systems for wmt18"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "an analysis of deep neural network models for practical applications"
    - "fake it till you make it fishing for catfishes"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-5 (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-6 (induced by source paper "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "on calibration of modern neural networks"
    - "molecular de novo design through deep reinforcement learning"
    - "confidence estimation in deep neural networks via density modelling"
    - "exploring loss function topology with cyclical learning rates"
    - "low data drug discovery with one shot learning"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "towards open set deep networks"

Connected through hyperedge HE-7 (induced by source paper "aleatoric and epistemic uncertainty in machine learning a tutorial introduction"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "epistemic uncertainty sampling"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-8 (induced by source paper "combining deep learning and verification for precise object instance detection"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "target driven instance detection"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "a dataset for developing and benchmarking active vision"
    - "synthesizing training data for object detection in indoor scenes"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

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)