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: "design challenges and misconceptions in neural sequence labeling"
  Abstract: We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.

Connected through hyperedge HE-1 (induced by source paper "hybrid neural tagging model for open relation extraction"):
  with members:
    - "an open world extension to knowledge graph completion models"
    - "graph neural networks with generated parameters for relation extraction"
    - "openki integrating open information extraction and knowledge bases with relation inference"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "a survey on open information extraction"
    - "neural open information extraction"
    - "deep active learning for named entity recognition"
    - "neural models for sequence chunking"

Connected through hyperedge HE-2 (induced by source paper "rethinking generalization of neural models a named entity recognition case study"):
  with members:
    - "a closer look at data bias in neural extractive summarization models"
    - "transfer learning for sequence labeling using source model and target data"
    - "stiffness a new perspective on generalization in neural networks"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-3 (induced by source paper "hierarchically refined label attention network for sequence labeling"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "two local models for neural constituent parsing"
    - "joint embedding of words and labels for text classification"
    - "semi supervised sequence modeling with cross view training"
    - "cross target stance classification with self attention networks"
    - "ncrf an open source neural sequence labeling toolkit"
    - "learning approximate inference networks for structured prediction"
    - "sentence state lstm for text representation"

Connected through hyperedge HE-4 (induced by source paper "can ner convolutional attention network for chinese named entity recognition"):
  with members:
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "chinese ner using lattice lstm"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial learning for chinese ner from crowd annotations"
    - "combining discrete and neural features for sequence labeling"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "benchmarking approximate inference methods for neural structured prediction"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "learning approximate inference networks for structured prediction"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning to embed words in context for syntactic tasks"
    - "classical structured prediction losses for sequence to sequence learning"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "grn gated relation network to enhance convolutional neural network for named entity recognition"):
  with members:
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-7 (induced by source paper "using similarity measures to select pretraining data for ner"):
  with members:
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "what s in a domain learning domain robust text representations using adversarial training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation"
    - "advances in pre training distributed word representations"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-8 (induced by source paper "using dynamic embeddings to improve static embeddings"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "transformer xl attentive language models beyond a fixed length context"
    - "simple bert models for relation extraction and semantic role labeling"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "linguistic knowledge and transferability of contextual representations"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"

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)