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: "hierarchical rnn with static sentence level attention for text based speaker change detection"
  Abstract: Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is useful in various scenarios, for example, processing dialog transcripts where speaker identities are missing (e.g., OpenSubtitle), and enhancing audio SCD with textual information. We formulate text-based SCD as a matching problem of utterances before and after a certain decision point; we propose a hierarchical recurrent neural network (RNN) with static sentence-level attention. Experimental results show that neural networks consistently achieve better performance than feature-based approaches, and that our attention-based model significantly outperforms non-attention neural networks.

Connected through hyperedge HE-1 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable and accurate deep learning for electronic health records"

Connected through hyperedge HE-2 (induced by source paper "incorporating interlocutor aware context into response generation on multi party chatbots"):
  with members:
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "a hybrid architecture for multi party conversational systems"
    - "a persona based neural conversation model"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-3 (induced by source paper "natural language interactions in autonomous vehicles intent detection and slot filling from passenger utterances"):
  with members:
    - "conversational intent understanding for passengers in autonomous vehicles"
    - "key value retrieval networks for task oriented dialogue"
    - "comparing human and machine errors in conversational speech transcription"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "bag of tricks for efficient text classification"
    - "achieving human parity in conversational speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "higru hierarchical gated recurrent units for utterance level emotion recognition"):
  with members:
    - "attention is all you need"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "bag of tricks for efficient text classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "discourse wizard discovering deep discourse structure in your conversation with rnns"):
  with members:
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "tensorflow a system for large scale machine learning"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-6 (induced by source paper "a context based approach for dialogue act recognition using simple recurrent neural networks"):
  with members:
    - "gradascent at emoint 2017 character and word level recurrent neural network models for tweet emotion intensity detection"
    - "learning to generate reviews and discovering sentiment"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "multiplicative lstm for sequence modelling"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-7 (induced by source paper "conversational intent understanding for passengers in autonomous vehicles"):
  with members:
    - "joint online spoken language understanding and language modeling with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "towards neural speaker modeling in multi party conversation the task dataset and models"):
  with members:
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "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"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "neural responding machine for short text conversation"
    - "attention with intention for a neural network conversation model"
    - "adam a method for stochastic optimization"

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)