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: "representing text chunks"
  Abstract: Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (Ramshaw and Marcus, 1995) have introduced a "convenient" data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the the data representation choice has a minor influence on chunking performance. However, equipped with the most suitable data representation, our memory-based learning chunker was able to improve the best published chunking results for a standard data set.

Connected through hyperedge HE-1 (induced by source paper "an efficient architecture for predicting the case of characters using sequence models"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "named entity recognition with bidirectional lstm cnns"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "graph convolution for multimodal information extraction from visually rich documents"):
  with members:
    - "n ary relation extraction using graph state lstm"
    - "chargrid towards understanding 2d documents"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "image generation from scene graphs"
    - "cross sentence n ary relation extraction with graph lstms"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-3 (induced by source paper "hierarchical multi task natural language understanding for cross domain conversational ai hermit nlu"):
  with members:
    - "benchmarking natural language understanding services for building conversational agents"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "deep contextualized word representations"
    - "towards dialogue based navigation with multivariate adaptation driven by intention and politeness for social robots"
    - "multi task learning for domain general spoken disfluency detection in dialogue systems"
    - "linguistically informed self attention for semantic role labeling"
    - "interactively picking real world objects with unconstrained spoken language instructions"

Connected through hyperedge HE-4 (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"
    - "robust lexical features for improved neural network named entity recognition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "character feature engineering for japanese word segmentation"):
  with members:
    - "chinese word segmentation another decade review 2007 2017"
    - "long short term memory for japanese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation learning for chinese"
    - "efficiently inducing features of conditional random fields"
    - "mostly unsupervised statistical segmentation of japanese kanji sequences"

Connected through hyperedge HE-6 (induced by source paper "incremental processing of noisy user utterances in the spoken language understanding task"):
  with members:
    - "multi task learning to improve natural language understanding"
    - "attention is all you need"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "zero shot cross lingual opinion target extraction"):
  with members:
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "elixa a modular and flexible absa platform"
    - "enriching word vectors with subword information"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "improving multi word entity recognition for biomedical texts"):
  with members:
    - "on the difficulty of training recurrent neural networks"

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)