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: "show your work improved reporting of experimental results"
  Abstract: Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone are insufficient for drawing accurate conclusions about which model performs best. We argue for reporting additional details, especially performance on validation data obtained during model development. We present a novel technique for doing so: expected validation performance of the best-found model as a function of computation budget (i.e., the number of hyperparameter search trials or the overall training time). Using our approach, we find multiple recent model comparisons where authors would have reached a different conclusion if they had used more (or less) computation. Our approach also allows us to estimate the amount of computation required to obtain a given accuracy; applying it to several recently published results yields massive variation across papers, from hours to weeks. We conclude with a set of best practices for reporting experimental results which allow for robust future comparisons, and provide code to allow researchers to use our technique.

Connected through hyperedge HE-1 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "question answering is a format when is it useful"
    - "understanding dataset design choices for multi hop reasoning"

Connected through hyperedge HE-2 (induced by source paper "a hierarchy of limitations in machine learning"):
  with members:
    - "case study predictive fairness to reduce misdemeanor recidivism through social service interventions"
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "the human body is a black box supporting clinical decision making with deep learning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "an open source automl benchmark"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "roles for computing in social change"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"

Connected through hyperedge HE-3 (induced by source paper "fine tuning pretrained language models weight initializations data orders and early stopping"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"
    - "freelb enhanced adversarial training for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"

Connected through hyperedge HE-4 (induced by source paper "document ranking with a pretrained sequence to sequence model"):
  with members:
    - "scaling laws for neural language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "multi stage document ranking with bert"
    - "passage re ranking with bert"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "zero shot text classification with generative language models"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-5 (induced by source paper "hulk an energy efficiency benchmark platform for responsible natural language processing"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "mlperf training benchmark"
    - "energy and policy considerations for deep learning in nlp"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "green ai"

Connected through hyperedge HE-6 (induced by source paper "optimizer benchmarking needs to account for hyperparameter tuning"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"
    - "energy and policy considerations for deep learning in nlp"
    - "an empirical study on hyperparameter tuning of decision trees"
    - "measuring the effects of data parallelism on neural network training"
    - "on the state of the art of evaluation in neural language models"
    - "decoupled weight decay regularization"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-7 (induced by source paper "olmpics on what language model pre training captures"):
  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"
    - "still a pain in the neck evaluating text representations on lexical composition"
    - "do nlp models know numbers probing numeracy in embeddings"
    - "learning and evaluating general linguistic intelligence"
    - "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"

Connected through hyperedge HE-8 (induced by source paper "green ai"):
  with members:
    - "sparse networks from scratch faster training without losing performance"
    - "defending against neural fake news"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

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)