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: "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
  Abstract: Building systems that can communicate with humans is a core problem in Artificial Intelligence. This work proposes a novel neural network architecture for response selection in an end-to-end multi-turn conversational dialogue setting. The architecture applies context level attention and incorporates additional external knowledge provided by descriptions of domain-specific words. It uses a bi-directional Gated Recurrent Unit (GRU) for encoding context and responses and learns to attend over the context words given the latent response representation and vice this http URL addition, it incorporates external domain specific information using another GRU for encoding the domain keyword descriptions. This allows better representation of domain-specific keywords in responses and hence improves the overall performance. Experimental results show that our model outperforms all other state-of-the-art methods for response selection in multi-turn conversations.

Connected through hyperedge HE-1 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "q8bert quantized 8bit bert"
    - "designing for health chatbots"
    - "are sixteen heads really better than one"
    - "dialog system technology challenge 7"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "extreme language model compression with optimal subwords and shared projections"

Connected through hyperedge HE-2 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "retrieve and refine improved sequence generation models for dialogue"

Connected through hyperedge HE-3 (induced by source paper "a repository of conversational datasets"):
  with members:
    - "designing for health chatbots"
    - "training neural response selection for task oriented dialogue systems"
    - "learning semantic textual similarity from conversations"
    - "fully statistical neural belief tracking"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "using a kg copy network for non goal oriented dialogues"):
  with members:
    - "incorporating joint embeddings into goal oriented dialogues with multi task learning"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "improving retrieval modeling using cross convolution networks and multi frequency word embedding"
    - "a trustworthy responsible and interpretable system to handle chit chat in conversational bots"
    - "a knowledge grounded neural conversation model"
    - "chat detection in an intelligent assistant combining task oriented and non task oriented spoken dialogue systems"
    - "flexible end to end dialogue system for knowledge grounded conversation"

Connected through hyperedge HE-5 (induced by source paper "incorporating joint embeddings into goal oriented dialogues with multi task learning"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "key value retrieval networks for task oriented dialogue"
    - "convolutional 2d knowledge graph embeddings"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"

Connected through hyperedge HE-6 (induced by source paper "domain adaptive training bert for response selection"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "interactive matching network for multi turn response selection in retrieval based chatbots"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "attention is all you need"
    - "enhanced lstm for natural language inference"

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.AI(Artificial Intelligence)