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: "should we really use post hoc tests based on mean ranks"
  Abstract: The statistical comparison of multiple algorithms over multiple data sets is fundamental in machine learning. This is typically carried out by the Friedman test. When the Friedman test rejects the null hypothesis, multiple comparisons are carried out to establish which are the significant differences among algorithms. The multiple comparisons are usually performed using the mean-ranks test. The aim of this technical note is to discuss the inconsistencies of the mean-ranks post-hoc test with the goal of discouraging its use in machine learning as well as in medicine, psychology, etc.. We show that the outcome of the mean-ranks test depends on the pool of algorithms originally included in the experiment. In other words, the outcome of the comparison between algorithms A and B depends also on the performance of the other algorithms included in the original experiment. This can lead to paradoxical situations. For instance the difference between A and B could be declared significant if the pool comprises algorithms C, D, E and not significant if the pool comprises algorithms F, G, H. To overcome these issues, we suggest instead to perform the multiple comparison using a test whose outcome only depends on the two algorithms being compared, such as the sign-test or the Wilcoxon signed-rank test.

Connected through hyperedge HE-1 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "inceptiontime finding alexnet for time series classification"
    - "towards a universal neural network encoder for time series"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "deep learning for time series analysis"
    - "multi scale convolutional neural networks for time series classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "an interpretable semi supervised classifier using two different strategies for amended self labeling"):
  with members:
    - "explaining explanations an overview of interpretability of machine learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "improved techniques for training gans"
    - "the mythos of model interpretability"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-3 (induced by source paper "deep learning for time series classification a review"):
  with members:
    - "detecting and interpreting myocardial infarction using fully convolutional neural networks"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "cost sensitive convolution based neural networks for imbalanced time series classification"
    - "genesis of basic and multi layer echo state network recurrent autoencoders for efficient data representations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "transfer learning for time series classification"

Connected through hyperedge HE-4 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification"):
  with members:
    - "scalable dictionary classifiers for time series classification"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "from bop to boss and beyond time series classification with dictionary based classifiers"
    - "scalable and accurate deep learning for electronic health records"
    - "the ucr time series archive"

Connected through hyperedge HE-5 (induced by source paper "deep neural network ensembles for time series classification"):
  with members:
    - "adversarial attacks on deep neural networks for time series classification"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep neural network ensemble by data augmentation and bagging for skin lesion classification"
    - "transfer learning for time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "towards a universal neural network encoder for time series"

Connected through hyperedge HE-6 (induced by source paper "investigating the effectiveness of word embedding based active learning for labelling text datasets"):
  with members:
    - "improving active learning in systematic reviews"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learned in translation contextualized word vectors"
    - "fasttext zip compressing text classification models"
    - "stability of topic modeling via matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for imbalanced classification"):
  with members:
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "imbalanced deep learning by minority class incremental rectification"
    - "reinforcement learning for relation classification from noisy data"
    - "deep reinforcement learning for list wise recommendations"
    - "focal loss for dense object detection"
    - "classification with costly features using deep reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "cost sensitive learning of deep feature representations from imbalanced data"

Connected through hyperedge HE-8 (induced by source paper "investigating the effectiveness of representations based on word embeddings in active learning for labelling text datasets"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "stability of topic modeling via matrix factorization"
    - "enriching word vectors with subword information"
    - "active discriminative text representation learning"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

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)