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: "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
  Abstract: In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10^-4) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F1-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we present network architectures that produce both superior performance as well as are more stable with respect to the remaining hyperparameters.

Connected through hyperedge HE-1 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning"):
  with members:
    - "tackling climate change with machine learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "accelerating deep learning by focusing on the biggest losers"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "quantifying the carbon emissions of machine learning"
    - "single headed attention rnn stop thinking with your head"
    - "green ai"

Connected through hyperedge HE-2 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

Connected through hyperedge HE-3 (induced by source paper "reflex flexible framework for relation extraction in multiple domains"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "hark side of deep learning from grad student descent to automated machine learning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "do imagenet classifiers generalize to imagenet"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "ngraph he a graph compiler for deep learning on homomorphically encrypted data"
    - "regularized evolution for image classifier architecture search"
    - "universal approximations of invariant maps by neural networks"
    - "do cifar 10 classifiers generalize to cifar 10"

Connected through hyperedge HE-5 (induced by source paper "better faster stronger sequence tagging constituent parsers"):
  with members:
    - "multilingual constituency parsing with self attention and pre training"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "deep contextualized word representations"
    - "constituent parsing as sequence labeling"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-6 (induced by source paper "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "correlating neural and symbolic representations of language"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"

Connected through hyperedge HE-7 (induced by source paper "show your work improved reporting of experimental results"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "model cards for model reporting"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "do cifar 10 classifiers generalize to cifar 10"

Connected through hyperedge HE-8 (induced by source paper "dependency guided lstm crf for named entity recognition"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "chinese ner using lattice lstm"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "robust lexical features for improved neural network named entity recognition"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"

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)