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: "dynamically context sensitive time decay attention for dialogue modeling"
  Abstract: Spoken language understanding (SLU) is an essential component in conversational systems. Considering that contexts provide informative cues for better understanding, history can be leveraged for contextual SLU. However, most prior work only paid attention to the related content in history utterances and ignored the temporal information. In dialogues, it is intuitive that the most recent utterances are more important than the least recent ones, and time-aware attention should be in a decaying manner. Therefore, this paper allows the model to automatically learn a time-decay attention function where the attentional weights can be dynamically decided based on the content of each role's contexts, which effectively integrates both content-aware and time-aware perspectives and demonstrates remarkable flexibility to complex dialogue contexts. The experiments on the benchmark Dialogue State Tracking Challenge (DSTC4) dataset show that the proposed dynamically context-sensitive time-decay attention mechanisms significantly improve the state-of-the-art model for contextual understanding performance.

Connected through hyperedge HE-1 (induced by source paper "bridging dialogue generation and facial expression synthesis"):
  with members:
    - "emotionlines an emotion corpus of multi party conversations"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "integrating planning for task completion dialogue policy learning"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "dual supervised learning for natural language understanding and generation"):
  with members:
    - "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"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"

Connected through hyperedge HE-3 (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"
    - "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"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"

Connected through hyperedge HE-4 (induced by source paper "decay function free time aware attention to context and speaker indicator for spoken language understanding"):
  with members:
    - "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"
    - "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"

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)