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: "zero shot cross lingual dialogue systems with transferable latent variables"
  Abstract: Despite the surging demands for multilingual task-oriented dialog systems (e.g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios. Hence, we propose a zero-shot adaptation of task-oriented dialogue system to low-resource languages. To tackle this challenge, we first use a set of very few parallel word pairs to refine the aligned cross-lingual word-level representations. We then employ a latent variable model to cope with the variance of similar sentences across different languages, which is induced by imperfect cross-lingual alignments and inherent differences in languages. Finally, the experimental results show that even though we utilize much less external resources, our model achieves better adaptation performance for natural language understanding task (i.e., the intent detection and slot filling) compared to the current state-of-the-art model in the zero-shot scenario.

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"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "efficient intent detection with dual sentence encoders"):
  with members:
    - "q8bert quantized 8bit bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "training neural response selection for task oriented dialogue systems"
    - "pretraining methods for dialog context representation learning"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "a repository of conversational datasets"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "transfer fine tuning a bert case study"

Connected through hyperedge HE-3 (induced by source paper "zero resource cross domain named entity recognition"):
  with members:
    - "do we need word order information for cross lingual sequence labeling"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "robust zero shot cross domain slot filling with example values"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "neural adaptation layers for cross domain named entity recognition"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-4 (induced by source paper "on the importance of word order information in cross lingual sequence labeling"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "how multilingual is multilingual bert"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "self attention with relative position representations"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-5 (induced by source paper "do we need word order information for cross lingual sequence labeling"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "self attention with relative position representations"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-6 (induced by source paper "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"):
  with members:
    - "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"
    - "xl nbt a cross lingual neural belief tracking framework"
    - "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"

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)