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: "toward mention detection robustness with recurrent neural networks"
  Abstract: One of the key challenges in natural language processing (NLP) is to yield good performance across application domains and languages. In this work, we investigate the robustness of the mention detection systems, one of the fundamental tasks in information extraction, via recurrent neural networks (RNNs). The advantage of RNNs over the traditional approaches is their capacity to capture long ranges of context and implicitly adapt the word embeddings, trained on a large corpus, into a task-specific word representation, but still preserve the original semantic generalization to be helpful across domains. Our systematic evaluation for RNN architectures demonstrates that RNNs not only outperform the best reported systems (up to 9\% relative error reduction) in the general setting but also achieve the state-of-the-art performance in the cross-domain setting for English. Regarding other languages, RNNs are significantly better than the traditional methods on the similar task of named entity recognition for Dutch (up to 22\% relative error reduction).

Connected through hyperedge HE-1 (induced by source paper "graph based neural networks for event factuality prediction using syntactic and semantic structures"):
  with members:
    - "one for all neural joint modeling of entities and events"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural models of factuality"
    - "exploring different dimensions of attention for uncertainty detection"
    - "multilingual modal sense classification using a convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

Connected through hyperedge HE-3 (induced by source paper "neural entity reasoner for global consistency in ner"):
  with members:
    - "chinese ner using lattice lstm"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "memory enhanced decoder for neural machine translation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural machine translation with external phrase memory"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "natural language processing for ehr based computational phenotyping"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "comparing rule based and deep learning models for patient phenotyping"
    - "unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "combining neural networks and log linear models to improve relation extraction"
    - "how essential are unstructured clinical narratives and information fusion to clinical trial recruitment"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"

Connected through hyperedge HE-5 (induced by source paper "deep active learning for named entity recognition"):
  with members:
    - "neural models for sequence chunking"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "globally normalized transition based neural networks"
    - "multi task cross lingual sequence tagging from scratch"

Connected through hyperedge HE-6 (induced by source paper "learning for biomedical information extraction methodological review of recent advances"):
  with members:
    - "deep convolutional networks for automated detection of posterior element fractures on spine ct"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "attention based convolutional neural network for machine comprehension"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"

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)