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 sensitive dual memory networks for multi turn slot tagging"
  Abstract: In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information. To incorporate dialog history, we present a neural architecture with Speaker-Sensitive Dual Memory Networks which encode utterances differently depending on the speaker. This addresses the different extents of information available to the system - the system knows only the surface form of user utterances while it has the exact semantics of system output. We performed experiments on real user data from Microsoft Cortana, a commercial personal assistant. The result showed a significant performance improvement over the state-of-the-art slot tagging models using contextual information.

Connected through hyperedge HE-1 (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 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-2 (induced by source paper "coupled representation learning for domains intents and slots in spoken language understanding"):
  with members:
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "efficient large scale domain classification with personalized attention"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "deep contextualized word representations"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "semi supervised classification with graph convolutional networks"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "distributed representations of words and phrases and their compositionality"

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)