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: "bayesian optimization of text representations"
  Abstract: When applying machine learning to problems in NLP, there are many choices to make about how to represent input texts. These choices can have a big effect on performance, but they are often uninteresting to researchers or practitioners who simply need a module that performs well. We propose an approach to optimizing over this space of choices, formulating the problem as global optimization. We apply a sequential model-based optimization technique and show that our method makes standard linear models competitive with more sophisticated, expensive state-of-the-art methods based on latent variable models or neural networks on various topic classification and sentiment analysis problems. Our approach is a first step towards black-box NLP systems that work with raw text and do not require manual tuning.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a sensitivity analysis of attention gated convolutional neural networks for sentence classification"):
  with members:
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "an attention gated convolutional neural network for sentence classification"
    - "attention is all you need"
    - "tensorflow a system for large scale machine learning"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "the effects of hyperparameters on sgd training of neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "sentiment and position taking analysis of parliamentary debates a systematic literature review"):
  with members:
    - "topic specific sentiment analysis can help identify political ideology"
    - "neural discourse structure for text categorization"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "stance and sentiment in tweets"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-4 (induced by source paper "a framework to integrate mode choice in the design of mobility on demand systems"):
  with members:
    - "impact of mobility on demand on traffic congestion simulation based study"
    - "the price of fragmentation in mobility on demand services"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "meta learning of textual representations"):
  with members:
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-6 (induced by source paper "sopa bridging cnns rnns and weighted finite state machines"):
  with members:
    - "recurrent additive networks"
    - "recurrent neural networks as weighted language recognizers"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "convolutional sequence to sequence learning"
    - "quasi recurrent neural networks"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through representation erasure"

Connected through hyperedge HE-7 (induced by source paper "learning the curriculum with bayesian optimization for task specific word representation learning"):
  with members:
    - "semantic word clusters using signed normalized graph cuts"
    - "neural architectures for named entity recognition"
    - "transition based dependency parsing with stack long short term memory"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "speed constrained tuning for statistical machine translation using bayesian optimization"):
  with members:
    - "bayesian optimisation for machine translation"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)