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: "jointly optimizing diversity and relevance in neural response generation"
  Abstract: Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the output of the conversation model, the improvement often comes at the cost of decreased relevance. In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. As a result, our approach induces a latent space in which the distance and direction from the predicted response vector roughly match the relevance and diversity, respectively. This property also lends itself well to an intuitive visualization of the latent space. Both automatic and human evaluation results demonstrate that the proposed approach brings significant improvement compared to strong baselines in both diversity and relevance.

Connected through hyperedge HE-1 (induced by source paper "towards a human like open domain chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "the evolved transformer"
    - "what makes a good conversation how controllable attributes affect human judgments"

Connected through hyperedge HE-2 (induced by source paper "avgout a simple output probability measure to eliminate dull responses"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "improving abstraction in text summarization"
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "polite dialogue generation without parallel data"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "learning to decode for future success"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-3 (induced by source paper "improving neural conversational models with entropy based data filtering"):
  with members:
    - "deep learning based chatbot models"
    - "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"
    - "training millions of personalized dialogue agents"

Connected through hyperedge HE-4 (induced by source paper "conversing by reading contentful neural conversation with on demand machine reading"):
  with members:
    - "dialog system technology challenge 7"
    - "a hybrid retrieval generation neural conversation model"
    - "challenges in building intelligent open domain dialog systems"
    - "consistent dialogue generation with self supervised feature learning"
    - "coqa a conversational question answering challenge"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"

Connected through hyperedge HE-5 (induced by source paper "investigating evaluation of open domain dialogue systems with human generated multiple references"):
  with members:
    - "re evaluating adem a deeper look at scoring dialogue responses"
    - "unifying human and statistical evaluation for natural language generation"
    - "survey on evaluation methods for dialogue systems"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "importance of a search strategy in neural dialogue modelling"
    - "texygen a benchmarking platform for text generation models"
    - "generating informative and diverse conversational responses via adversarial information maximization"

Connected through hyperedge HE-6 (induced by source paper "consistent dialogue generation with self supervised feature learning"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "dialogue natural language inference"
    - "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-7 (induced by source paper "structuring latent spaces for stylized response generation"):
  with members:
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "multi task deep neural networks for natural language understanding"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "unsupervised text style transfer using language models as discriminators"
    - "the natural language decathlon multitask learning as question answering"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-8 (induced by source paper "dialogpt large scale generative pre training for conversational response generation"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "dlgnet a transformer based model for dialogue response generation"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "consistent dialogue generation with self supervised feature learning"
    - "structuring latent spaces for stylized response generation"
    - "investigating evaluation of open domain dialogue systems with human generated multiple references"

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)