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: "xl nbt a cross lingual neural belief tracking framework"
  Abstract: Task-oriented dialog systems are becoming pervasive, and many companies heavily rely on them to complement human agents for customer service in call centers. With globalization, the need for providing cross-lingual customer support becomes more urgent than ever. However, cross-lingual support poses great challenges---it requires a large amount of additional annotated data from native speakers. In order to bypass the expensive human annotation and achieve the first step towards the ultimate goal of building a universal dialog system, we set out to build a cross-lingual state tracking framework. Specifically, we assume that there exists a source language with dialog belief tracking annotations while the target languages have no annotated dialog data of any form. Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data. We then distill and transfer its own knowledge to the student state tracker in target languages. We specifically discuss two types of common parallel resources: bilingual corpus and bilingual dictionary, and design different transfer learning strategies accordingly. Experimentally, we successfully use English state tracker as the teacher to transfer its knowledge to both Italian and German trackers and achieve promising results.

Connected through hyperedge HE-1 (induced by source paper "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "towards a human like open domain chatbot"
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"

Connected through hyperedge HE-2 (induced by source paper "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"):
  with members:
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "universal transformers"
    - "cross lingual language model pretraining"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"

Connected through hyperedge HE-3 (induced by source paper "zero shot cross lingual dialogue systems with transferable latent variables"):
  with members:
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "universal neural machine translation for extremely low resource languages"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "latent intention dialogue models"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-4 (induced by source paper "semantically conditioned dialog response generation via hierarchical disentangled self attention"):
  with members:
    - "global locally self attentive dialogue state tracker"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"
    - "latent intention dialogue models"
    - "attention is all you need"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "multi domain neural network language generation for spoken dialogue systems"

Connected through hyperedge HE-5 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "pytext a seamless path from nlp research to production"
    - "on the strength of character language models for multilingual named entity recognition"
    - "parameter sharing between dependency parsers for related languages"
    - "zero shot cross lingual classification using multilingual neural machine translation"

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)