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: "discourse wizard discovering deep discourse structure in your conversation with rnns"
  Abstract: Spoken language understanding is one of the key factors in a dialogue system, and a context in a conversation plays an important role to understand the current utterance. In this work, we demonstrate the importance of context within the dialogue for neural network models through an online web interface live demo. We developed two different neural models: a model that does not use context and a context-based model. The no-context model classifies dialogue acts at an utterance-level whereas the context-based model takes some preceding utterances into account. We make these trained neural models available as a live demo called Discourse-Wizard using a modular server architecture. The live demo provides an easy to use interface for conversational analysis and for discovering deep discourse structures in a conversation.

Connected through hyperedge HE-1 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators"):
  with members:
    - "incorporating end to end speech recognition models for sentiment analysis"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "emotionlines an emotion corpus of multi party conversations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "deep contextualized word representations"
    - "neural machine translation in linear time"

Connected through hyperedge HE-2 (induced by source paper "enriching existing conversational emotion datasets with dialogue acts using neural annotators"):
  with members:
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "emotionlines an emotion corpus of multi party conversations"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "deep contextualized word representations"
    - "neural machine translation in linear time"

Connected through hyperedge HE-3 (induced by source paper "towards dialogue based navigation with multivariate adaptation driven by intention and politeness for social robots"):
  with members:
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "sentiment adaptive end to end dialog systems"
    - "enabling a pepper robot to provide automated and interactive tours of a robotics laboratory"
    - "setting up pepper for autonomous navigation and personalized interaction with users"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "end to end joint learning of natural language understanding and dialogue manager"
    - "a computational approach to politeness with application to social factors"

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)