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: "a multimodal lstm for predicting listener empathic responses over time"
  Abstract: People naturally understand the emotions of-and often also empathize with-those around them. In this paper, we predict the emotional valence of an empathic listener over time as they listen to a speaker narrating a life story. We use the dataset provided by the OMG-Empathy Prediction Challenge, a workshop held in conjunction with IEEE FG 2019. We present a multimodal LSTM model with feature-level fusion and local attention that predicts empathic responses from audio, text, and visual features. Our best-performing model, which used only the audio and text features, achieved a concordance correlation coefficient (CCC) of 0.29 and 0.32 on the Validation set for the Generalized and Personalized track respectively, and achieved a CCC of 0.14 and 0.14 on the held-out Test set. We discuss the difficulties faced and the lessons learnt tackling this challenge.

Connected through hyperedge HE-1 (induced by source paper "modeling emotion in complex stories the stanford emotional narratives dataset"):
  with members:
    - "applying probabilistic programming to affective computing"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "attending to emotional narratives"
    - "the omg emotion behavior dataset"
    - "multimodal generative models for scalable weakly supervised learning"
    - "pomegranate fast and flexible probabilistic modeling in python"

Connected through hyperedge HE-2 (induced by source paper "beyond turing intelligent agents centered on the user"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "talking to myself self dialogues as data for conversational agents"
    - "leveraging user engagement signals for entity labeling in a virtual assistant"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "conversational ai the science behind the alexa prize"
    - "laying down the yellow brick road development of a wizard of oz interface for collecting human robot dialogue"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-3 (induced by source paper "attending to emotional narratives"):
  with members:
    - "modeling emotion in complex stories the stanford emotional narratives dataset"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memory fusion network for multi view sequential learning"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "how deep neural networks can improve emotion recognition on video data"

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)