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: "flexible end to end dialogue system for knowledge grounded conversation"
  Abstract: In knowledge grounded conversation, domain knowledge plays an important role in a special domain such as Music. The response of knowledge grounded conversation might contain multiple answer entities or no entity at all. Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities. We propose a fully data-driven generative dialogue system GenDS that is capable of generating responses based on input message and related knowledge base (KB). To generate arbitrary number of answer entities even when these entities never appear in the training set, we design a dynamic knowledge enquirer which selects different answer entities at different positions in a single response, according to different local context. It does not rely on the representations of entities, enabling our model deal with out-of-vocabulary entities. We collect a human-human conversation data (ConversMusic) with knowledge annotations. The proposed method is evaluated on CoversMusic and a public question answering dataset. Our proposed GenDS system outperforms baseline methods significantly in terms of the BLEU, entity accuracy, entity recall and human evaluation. Moreover,the experiments also demonstrate that GenDS works better even on small datasets.

Connected through hyperedge HE-1 (induced by source paper "improving neural conversational models with entropy based data filtering"):
  with members:
    - "deep learning based chatbot models"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "neural response ranking for social conversation a data efficient approach"
    - "automatic evaluation of neural personality based chatbots"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"

Connected through hyperedge HE-2 (induced by source paper "refnet a reference aware network for background based conversation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "challenges in building intelligent open domain dialog systems"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "first principle study on honeycomb fluorated inte monolayer with large rashba spin splitting and direct bandgap"
    - "improving background based conversation with context aware knowledge pre selection"
    - "learning to select knowledge for response generation in dialog systems"

Connected through hyperedge HE-3 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "real or fake learning to discriminate machine from human generated text"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-4 (induced by source paper "thinking globally acting locally distantly supervised global to local knowledge selection for background based conversation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "refnet a reference aware network for background based conversation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "improving background based conversation with context aware knowledge pre selection"
    - "learning to select knowledge for response generation in dialog systems"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (induced by source paper "dykgchat benchmarking dialogue generation grounding on dynamic knowledge graphs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "variational knowledge graph reasoning"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "key value retrieval networks for task oriented dialogue"
    - "a knowledge grounded neural conversation model"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

Connected through hyperedge HE-6 (induced by source paper "proposal towards a personalized knowledge powered self play based ensemble dialog system"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "improving neural conversational models with entropy based data filtering"
    - "deep learning based chatbot models"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "exploration by random network distillation"
    - "advancing the state of the art in open domain dialog systems through the alexa prize"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-7 (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"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "conversation generation with concept flow"):
  with members:
    - "the curious case of neural text degeneration"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "neural text generation with unlikelihood training"
    - "target guided open domain conversation"
    - "deep contextualized word representations"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "a dataset for document grounded conversations"
    - "generating informative and diverse conversational responses via adversarial information maximization"

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)