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: "speaker role contextual modeling for language understanding and dialogue policy learning"
  Abstract: Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks.

Connected through hyperedge HE-1 (induced by source paper "alternating recurrent dialog model with large scale pre trained language models"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "dual supervised learning for natural language understanding and generation"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "integrating planning for task completion dialogue policy learning"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "dual supervised learning"
    - "listening while speaking speech chain by deep learning"

Connected through hyperedge HE-3 (induced by source paper "generative dialog policy for task oriented dialog systems"):
  with members:
    - "integrating planning for task completion dialogue policy learning"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"
    - "a network based end to end trainable task oriented dialogue system"
    - "continuously learning neural dialogue management"

Connected through hyperedge HE-4 (induced by source paper "modeling inter speaker relationship in xlnet for contextual spoken language understanding"):
  with members:
    - "decay function free time aware attention to context and speaker indicator for spoken language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"

Connected through hyperedge HE-5 (induced by source paper "decay function free time aware attention to context and speaker indicator for spoken language understanding"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "speaker sensitive dual memory networks for multi turn slot tagging"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "investigating linguistic pattern ordering in hierarchical natural language generation"):
  with members:
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "end to end task completion neural dialogue systems"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "the e2e dataset new challenges for end to end generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "learning end to end goal oriented dialog"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "dynamically context sensitive time decay attention for dialogue modeling"):
  with members:
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "end to end task completion neural dialogue systems"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "knowledge as a teacher knowledge guided structural attention networks"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "dynamic memory networks for visual and textual question answering"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-8 (induced by source paper "addressee and response selection in multi party conversations with speaker interaction rnns"):
  with members:
    - "towards neural speaker modeling in multi party conversation the task dataset and models"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "a persona based neural conversation model"
    - "a network based end to end trainable task oriented dialogue system"
    - "coherent dialogue with attention based language models"
    - "learning end to end goal oriented dialog"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

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)