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: "dialogue natural language inference"
  Abstract: Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model's consistency.

Connected through hyperedge HE-1 (induced by source paper "attention over parameters for dialogue systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "moel mixture of empathetic listeners"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "caire an end to end empathetic chatbot"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "transferable multi domain state generator for task oriented dialogue systems"

Connected through hyperedge HE-2 (induced by source paper "the second conversational intelligence challenge convai2"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "data augmentation for neural online chat response selection"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "consistent dialogue generation with self supervised feature learning"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (induced by source paper "sketch fill a r a persona grounded chit chat generation framework"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "global to local memory pointer networks for task oriented dialogue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-5 (induced by source paper "generating persona consistent dialogues by exploiting natural language inference"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "exploiting persona information for diverse generation of conversational responses"
    - "evaluating coherence in dialogue systems using entailment"
    - "a study of reinforcement learning for neural machine translation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"

Connected through hyperedge HE-6 (induced by source paper "mmm multi stage multi task learning for multi choice reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "dynamically fused graph network for multi hop reasoning"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-7 (induced by source paper "getting to know you user attribute extraction from dialogues"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "training millions of personalized dialogue agents"

Connected through hyperedge HE-8 (induced by source paper "personalizing dialogue agents via meta learning"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "global to local memory pointer networks for task oriented dialogue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "training millions of personalized dialogue agents"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "multi level memory for task oriented dialogs"

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)