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: "quantifying uncertainties in natural language processing tasks"
  Abstract: Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.

Connected through hyperedge HE-1 (induced by source paper "exploring uncertainty measures for image caption embedding and retrieval task"):
  with members:
    - "a closer look at few shot classification"
    - "exploring uncertainty in conditional multi modal retrieval systems"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "object hallucination in image captioning"
    - "anomaly machine component detection by deep generative model with unregularized score"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-2 (induced by source paper "improving back translation with uncertainty based confidence estimation"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "investigating backtranslation in neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "confidence modeling for neural semantic parsing"
    - "triangular architecture for rare language translation"
    - "phrase based neural unsupervised machine translation"
    - "modeling uncertainty with hedged instance embedding"
    - "understanding back translation at scale"

Connected through hyperedge HE-3 (induced by source paper "model based interactive semantic parsing a unified framework and a text to sql case study"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "learning to learn semantic parsers from natural language supervision"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sql to text generation with graph to sequence model"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"

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)