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: "confidence modeling for neural semantic parsing"
  Abstract: In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used to estimate confidence scores that indicate whether model predictions are likely to be correct. Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying on attention scores.

Connected through hyperedge HE-1 (induced by source paper "deep bayesian active learning for multiple correct outputs"):
  with members:
    - "why does a visual question have different answers"
    - "bertscore evaluating text generation with bert"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"
    - "deep active learning for named entity recognition"
    - "learning by asking questions"
    - "active learning for visual question answering an empirical study"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "controlling risk of web question answering"):
  with members:
    - "densely connected attention propagation for reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "reducing network agnostophobia"
    - "know what you don t know unanswerable questions for squad"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "confidence from invariance to image transformations"

Connected through hyperedge HE-3 (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"
    - "quantifying uncertainties in natural language processing tasks"
    - "achieving human parity on automatic chinese to english news translation"
    - "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-4 (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"
    - "quantifying uncertainties in natural language processing tasks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "robust spoken language understanding via paraphrasing"):
  with members:
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "language to logical form with neural attention"
    - "data recombination for neural semantic parsing"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "a neural attention model for abstractive sentence summarization"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"

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)