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: "consistent dialogue generation with self supervised feature learning"
  Abstract: Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. In this paper, we propose a neural conversation model that generates consistent responses by maintaining certain features related to topics and personas throughout the conversation. Unlike past work that requires external supervision such as user identities, which are often unavailable or classified as sensitive information, our approach trains topic and persona feature extractors in a self-supervised way by utilizing the natural structure of dialogue data. Moreover, we adopt a binary feature representation and introduce a feature disentangling loss which, paired with controllable response generation techniques, allows us to promote or demote certain learned topics and personas features. The evaluation result demonstrates the model's capability of capturing meaningful topics and personas features, and the incorporation of the learned features brings significant improvement in terms of the quality of generated responses on two datasets, even comparing with model which explicit persona information.

Connected through hyperedge HE-1 (induced by source paper "conversing by reading contentful neural conversation with on demand machine reading"):
  with members:
    - "dialog system technology challenge 7"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "a hybrid retrieval generation neural conversation model"
    - "challenges in building intelligent open domain dialog systems"
    - "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-2 (induced by source paper "inset sentence infilling with inter sentential generative pre training"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "linguistic versus latent relations for modeling coherent flow in paragraphs"
    - "the curious case of neural text degeneration"
    - "what makes a good story designing composite rewards for visual storytelling"
    - "spanbert improving pre training by representing and predicting spans"
    - "abductive reasoning as self supervision for common sense question answering"
    - "sentence bert sentence embeddings using siamese bert networks"

Connected through hyperedge HE-3 (induced by source paper "domain adaptive text style transfer"):
  with members:
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"

Connected through hyperedge HE-4 (induced by source paper "structuring latent spaces for stylized response generation"):
  with members:
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "multi task deep neural networks for natural language understanding"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "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-5 (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"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "structuring latent spaces for stylized response generation"
    - "investigating evaluation of open domain dialogue systems with human generated multiple references"

Connected through hyperedge HE-6 (induced by source paper "jointly optimizing diversity and relevance in neural response generation"):
  with members:
    - "dialog system technology challenge 7"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "a knowledge grounded neural conversation model"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "transformation of dense and sparse text representations"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "investigating capsule networks with dynamic routing for text classification"
    - "attention is all you need"
    - "sparse representation based open set recognition"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "spine sparse interpretable neural embeddings"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)