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: "robust lexical features for improved neural network named entity recognition"
  Abstract: Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception of gazetteers. In this work, we show that this is unfair: lexical features are actually quite useful. We propose to embed words and entity types into a low-dimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia. From this, we compute - offline - a feature vector representing each word. When used with a vanilla recurrent neural network model, this representation yields substantial improvements. We establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while matching state-of-the-art performance with a F1 score of 91.73 on the over-studied CONLL-2003 dataset.

Connected through hyperedge HE-1 (induced by source paper "attention based conditioning methods for external knowledge integration"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "gating mechanisms for combining character and word level word representations an empirical study"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "visual reasoning with multi hop feature modulation"

Connected through hyperedge HE-2 (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  with members:
    - "semantics aware bert for language understanding"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "syntax aware multilingual semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "contextualized non local neural networks for sequence learning"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "tener adapting transformer encoder for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "sequence labeling a practical approach"
    - "chinese ner using lattice lstm"
    - "self attention with relative position representations"
    - "contextualized non local neural networks for sequence learning"
    - "hybrid semi markov crf for neural sequence labeling"

Connected through hyperedge HE-4 (induced by source paper "dependency guided lstm crf for named entity recognition"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "chinese ner using lattice lstm"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "why attention analyze bilstm deficiency and its remedies in the case of ner"):
  with members:
    - "discontinuous constituency parsing with a stack free transition system and a dynamic oracle"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "towards open domain named entity recognition via neural correction models"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "cloze driven pretraining of self attention networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "evaluating the utility of hand crafted features in sequence labelling"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "towards lingua franca named entity recognition with bert"):
  with members:
    - "cloze driven pretraining of self attention networks"
    - "how multilingual is multilingual bert"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "neural cross lingual named entity recognition with minimal resources"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "scrubbing sensitive phi data from medical records made easy by spacy a scalable model implementation comparisons"):
  with members:
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"
    - "phi scrubber a deep learning approach"
    - "fast and accurate entity recognition with iterated dilated convolutions"

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)