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: "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
  Abstract: Developing state-of-the-art approaches for specific tasks is a major driving force in our research community. Depending on the prestige of the task, publishing it can come along with a lot of visibility. The question arises how reliable are our evaluation methodologies to compare approaches?#R##N#One common methodology to identify the state-of-the-art is to partition data into a train, a development and a test set. Researchers can train and tune their approach on some part of the dataset and then select the model that worked best on the development set for a final evaluation on unseen test data. Test scores from different approaches are compared, and performance differences are tested for statistical significance.#R##N#In this publication, we show that there is a high risk that a statistical significance in this type of evaluation is not due to a superior learning approach. Instead, there is a high risk that the difference is due to chance. For example for the CoNLL 2003 NER dataset we observed in up to 26% of the cases type I errors (false positives) with a threshold of p < 0.05, i.e., falsely concluding a statistically significant difference between two identical approaches.#R##N#We prove that this evaluation setup is unsuitable to compare learning approaches. We formalize alternative evaluation setups based on score distributions.

Connected through hyperedge HE-1 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers"):
  with members:
    - "using word embeddings to examine gender bias in dutch newspapers 1950 1990"
    - "the via annotation software for images audio and video"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "sentence bert sentence embeddings using siamese bert networks"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "on measuring social biases in sentence encoders"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "bertscore evaluating text generation with bert"
    - "understanding the behaviors of bert in ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning semantic textual similarity from conversations"

Connected through hyperedge HE-3 (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-4 (induced by source paper "fiesta fast identification of state of the art models using adaptive bandit algorithms"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "allennlp a deep semantic natural language processing platform"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "interactive attention networks for aspect level sentiment classification"
    - "simple bayesian algorithms for best arm identification"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-5 (induced by source paper "the non utility of structural features in bilstm based dependency parsers"):
  with members:
    - "recursive subtree composition in lstm based dependency parsing"
    - "what s going on in neural constituency parsers an analysis"
    - "transition based parsing with lighter feed forward networks"
    - "global transition based non projective dependency parsing"
    - "deep rnns encode soft hierarchical syntax"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "an investigation of the interactions between pre trained word embeddings character models and pos tags in dependency parsing"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"

Connected through hyperedge HE-6 (induced by source paper "a neural model for dialogue coherence assessment"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "multi task learning for coherence modeling"
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "measuring semantic coherence of a conversation"
    - "coherence models for dialogue"

Connected through hyperedge HE-7 (induced by source paper "classification and clustering of arguments with contextualized word embeddings"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "measuring the similarity of sentential arguments in dialogue"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "clearumor at semeval 2019 task 7 convolving elmo against rumors"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)