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: "introduction to the conll 2000 shared task chunking"
  Abstract: We describe the CoNLL-2000 shared task: dividing text into syntactically related non-overlapping groups of words, so-called text chunking. We give background information on the data sets, present a general overview of the systems that have taken part in the shared task and briefly discuss their performance.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "augmenting neural networks with first order logic"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "backpropagating through structured argmax using a spigot"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "towards semi supervised learning for deep semantic role labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarially regularising neural nli models to integrate logical background knowledge"

Connected through hyperedge HE-3 (induced by source paper "gcdt a global context enhanced deep transition architecture for sequence labeling"):
  with members:
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "neural reranking for named entity recognition"
    - "attention is all you need"
    - "neural models for sequence chunking"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "evaluating word embedding models methods and experimental results"):
  with members:
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "post processing of word representations via variance normalization and dynamic embedding"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"

Connected through hyperedge HE-6 (induced by source paper "text processing like humans do visually attacking and shielding nlp systems"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "shielding google s language toxicity model against adversarial attacks"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "deceiving google s perspective api built for detecting toxic comments"

Connected through hyperedge HE-7 (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"
    - "design challenges and misconceptions in neural sequence labeling"

Connected through hyperedge HE-8 (induced by source paper "shallow syntax in deep water"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "comprehensive supersense disambiguation of english prepositions and possessives"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "neural models of factuality"

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)