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: "why are sequence to sequence models so dull understanding the low diversity problem of chatbots"
  Abstract: Diversity is a long-studied topic in information retrieval that usually refers to the requirement that retrieved results should be non-repetitive and cover different aspects. In a conversational setting, an additional dimension of diversity matters: an engaging response generation system should be able to output responses that are diverse and interesting. Sequence-to-sequence (Seq2Seq) models have been shown to be very effective for response generation. However, dialogue responses generated by Seq2Seq models tend to have low diversity. In this paper, we review known sources and existing approaches to this low-diversity problem. We also identify a source of low diversity that has been little studied so far, namely model over-confidence. We sketch several directions for tackling model over-confidence and, hence, the low-diversity problem, including confidence penalties and label smoothing.

Connected through hyperedge HE-1 (induced by source paper "do massively pretrained language models make better storytellers"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "the curious case of neural text degeneration"
    - "improving neural story generation by targeted common sense grounding"
    - "unifying human and statistical evaluation for natural language generation"
    - "encoder agnostic adaptation for conditional language generation"
    - "gltr statistical detection and visualization of generated text"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "importance of search and evaluation strategies in neural dialogue modeling"

Connected through hyperedge HE-2 (induced by source paper "exploiting persona information for diverse generation of conversational responses"):
  with members:
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "learning end to end goal oriented dialog with multiple answers"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "improving neural response diversity with frequency aware cross entropy loss"):
  with members:
    - "dialogue generation from imitation learning to inverse reinforcement learning"
    - "regularizing neural networks by penalizing confident output distributions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "effective approaches to attention based neural machine translation"
    - "a neural network approach to context sensitive generation of conversational responses"

Connected through hyperedge HE-4 (induced by source paper "a study of state aliasing in structured prediction with rnns"):
  with members:
    - "textworld a learning environment for text based games"
    - "a study of reinforcement learning for neural machine translation"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "an actor critic algorithm for sequence prediction"
    - "a sequence to sequence model for user simulation in spoken dialogue systems"
    - "deep reinforcement learning with a natural language action space"

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)