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: "modeling noisiness to recognize named entities using multitask neural networks on social media"
  Abstract: Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments.

Connected through hyperedge HE-1 (induced by source paper "on identifying hashtags in disaster twitter data"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "deep contextualized word representations"
    - "domain adaptation with adversarial training and graph embeddings"
    - "recurrent neural network for text classification with multi task learning"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-2 (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"
    - "robust lexical features for improved neural network named entity recognition"
    - "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-3 (induced by source paper "dependency aware named entity recognition with relative and global attentions"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient dependency guided named entity recognition"
    - "attention is all you need"
    - "empower sequence labeling with task aware neural language model"
    - "learning word segmentation representations to improve named entity recognition for chinese social media"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "keyphrase extraction from disaster related tweets"):
  with members:
    - "deep contextualized word representations"
    - "dissecting contextual word embeddings architecture and representation"
    - "deep keyphrase generation"
    - "semi supervised discovery of informative tweets during the emerging disasters"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "recurrent neural network for text classification with multi task learning"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-5 (induced by source paper "named entity recognition on code switched data overview of the calcs 2018 shared task"):
  with members:
    - "part of speech tagging for code switched data"
    - "a multi task approach for named entity recognition in social media data"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "analysis of named entity recognition and linking for tweets"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "robust named entity recognition with truecasing pretraining"):
  with members:
    - "robustness to capitalization errors in named entity recognition"
    - "ner and pos when nothing is capitalized"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-7 (induced by source paper "why attention analyzing and remedying bilstm deficiency in modeling cross context for ner"):
  with members:
    - "semi supervised sequence modeling with cross view training"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "named entity recognition with bidirectional lstm cnns"

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)