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: "efficient dependency guided named entity recognition"
  Abstract: Named entity recognition (NER), which focuses on the extraction of semantically meaningful named entities and their semantic classes from text, serves as an indispensable component for several down-stream natural language processing (NLP) tasks such as relation extraction and event extraction. Dependency trees, on the other hand, also convey crucial semantic-level information. It has been shown previously that such information can be used to improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012). In this work, we investigate on how to better utilize the structured information conveyed by dependency trees to improve the performance of NER. Specifically, unlike existing approaches which only exploit dependency information for designing local features, we show that certain global structured information of the dependency trees can be exploited when building NER models where such information can provide guided learning and inference. Through extensive experiments, we show that our proposed novel dependency-guided NER model performs competitively with models based on conventional semi-Markov conditional random fields, while requiring significantly less running time.

Connected through hyperedge HE-1 (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"
    - "robust lexical features for improved neural network named entity recognition"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"

Connected through hyperedge HE-2 (induced by source paper "faster shift reduce constituent parsing with a non binary bottom up strategy"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "syntactic scaffolds for semantic structures"
    - "a tree based decoder for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "dependency aware named entity recognition with relative and global attentions"):
  with members:
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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 "near or far wide range zero shot cross lingual dependency parsing"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "deep contextualized word representations"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "neural cross lingual coreference resolution and its application to entity linking"
    - "neural cross lingual named entity recognition with minimal resources"

Connected through hyperedge HE-5 (induced by source paper "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "neural cross lingual coreference resolution and its application to entity linking"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

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)