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: "negative training for neural dialogue response generation"
  Abstract: Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses and generic (boring) responses. In this work, we propose a framework named "Negative Training" to minimize such behaviors. Given a trained model, the framework will first find generated samples that exhibit the undesirable behavior, and then use them to feed negative training signals for fine-tuning the model. Our experiments show that negative training can significantly reduce the hit rate of malicious responses (e.g. from 12.6% to 0%), or discourage frequent responses and improve response diversity (e.g. improve response entropy by over 63%).

Connected through hyperedge HE-1 (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-2 (induced by source paper "neural text generation with unlikelihood training"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "the curious case of neural text degeneration"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "learning to write with cooperative discriminators"

Connected through hyperedge HE-3 (induced by source paper "don t say that making inconsistent dialogue unlikely with unlikelihood training"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "hellaswag can a machine really finish your sentence"
    - "language models as knowledge bases"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "the curious case of neural text degeneration"
    - "latent relation language models"
    - "counterfactual story reasoning and generation"
    - "cooperative generator discriminator networks for abstractive summarization with narrative flow"

Connected through hyperedge HE-4 (induced by source paper "caire an end to end empathetic chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "moel mixture of empathetic listeners"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"

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)