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: "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
  Abstract: Spoken language understanding (SLU) is an essential component in conversational systems. Most SLU component treats each utterance independently, and then the following components aggregate the multi-turn information in the separate phases. In order to avoid error propagation and effectively utilize contexts, prior work leveraged history for contextual SLU. However, the previous model only paid attention to the content in history utterances without considering their temporal information and speaker roles. In the dialogues, the most recent utterances should be more important than the least recent ones. Furthermore, users usually pay attention to 1) self history for reasoning and 2) others' utterances for listening, the speaker of the utterances may provides informative cues to help understanding. Therefore, this paper proposes an attention-based network that additionally leverages temporal information and speaker role for better SLU, where the attention to contexts and speaker roles can be automatically learned in an end-to-end manner. The experiments on the benchmark Dialogue State Tracking Challenge 4 (DSTC4) dataset show that the time-aware dynamic role attention networks significantly improve the understanding performance.

Connected through hyperedge HE-1 (induced by source paper "a modular task oriented dialogue system using a neural mixture of experts"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "multi source domain adaptation with mixture of experts"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "understanding back translation at scale"
    - "key value retrieval networks for task oriented dialogue"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "a survey on dialogue systems recent advances and new frontiers"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "sentnet source aware recurrent entity network for dialogue response selection"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a persona based neural conversation model"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "tracking the world state with recurrent entity networks"

Connected through hyperedge HE-5 (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"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"

Connected through hyperedge HE-6 (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"
    - "speaker sensitive dual memory networks for multi turn slot tagging"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "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-7 (induced by source paper "time masking leveraging temporal information in spoken dialogue systems"):
  with members:
    - "contextual slot carryover for disparate schemas"
    - "scalable multi domain dialogue state tracking"
    - "context dependent word representation for neural machine translation"
    - "neural belief tracker data driven dialogue state tracking"

Connected through hyperedge HE-8 (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"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "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"

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)