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: "hierarchical text generation and planning for strategic dialogue"
  Abstract: End-to-end models for strategic dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors. We introduce an approach to generating latent representations of dialogue moves, by inducing sentence representations to maximize the likelihood of subsequent sentences and actions. The effect is to decouple much of the semantics of the utterance from its linguistic realisation. We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning. Experiments show that using our message representations increases the reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language. Our hierarchical latent-variable model outperforms previous work both linguistically and strategically.

Connected through hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system"):
  with members:
    - "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "domain adaptive dialog generation via meta learning"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "unsupervised dialog structure learning"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"

Connected through hyperedge HE-2 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "hierarchical decision making by generating and following natural language instructions"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "countering language drift via visual grounding"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "target guided open domain conversation"
    - "learning semantic textual similarity from conversations"

Connected through hyperedge HE-3 (induced by source paper "dynamic knowledge routing network for target guided open domain conversation"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "end to end knowledge routed relational dialogue system for automatic diagnosis"
    - "target guided open domain conversation"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "towards deep conversational recommendations"
    - "emergent communication through negotiation"
    - "conversational ai the science behind the alexa prize"

Connected through hyperedge HE-4 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "on accurate evaluation of gans for language generation"
    - "neural text generation past present and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"

Connected through hyperedge HE-5 (induced by source paper "target guided open domain conversation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "towards deep conversational recommendations"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "decoupling strategy and generation in negotiation dialogues"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-6 (induced by source paper "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"):
  with members:
    - "towards universal dialogue state tracking"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "latent variable dialogue models and their diversity"

Connected through hyperedge HE-7 (induced by source paper "mala cross domain dialogue generation with action learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "carl aggregated search with context aware module embedding learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "unsupervised text style transfer using language models as discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"

Connected through hyperedge HE-8 (induced by source paper "know more about each other evolving dialogue strategy via compound assessment"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "chat more if you like dynamic cue words planning to flow longer conversations"
    - "a knowledge grounded neural conversation model"
    - "dailydialog a manually labelled multi turn dialogue dataset"

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)