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: "annotating and modeling empathy in spoken conversations"
  Abstract: Abstract   Empathy, as defined in behavioral sciences, expresses the ability of human beings to recognize, understand and react to emotions, attitudes and beliefs of others. In this paper, we address two related problems in automatic affective behavior analysis: the design of the annotation protocol and the automatic recognition of empathy from human–human dyadic spoken conversations. We propose and evaluate an annotation scheme for empathy inspired by the  modal  model of emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic spoken conversations. In the context of behavioral analysis, we designed an automatic segmentation and classification system for empathy. Given the different speech and language levels of representation where empathy may be communicated, we investigated features derived from the lexical and acoustic spaces. The feature development process was designed to support both the fusion and automatic selection of relevant features from a high dimensional space. The automatic classification system was evaluated on call center conversations where it showed significantly better performance than the baseline.

Connected through hyperedge HE-1 (induced by source paper "learning word ratings for empathy and distress from document level user responses"):
  with members:
    - "joint emotion label space modelling for affect lexica"
    - "modeling empathy and distress in reaction to news stories"
    - "aff2vec affect enriched distributional word representations"
    - "advances in pre training distributed word representations"
    - "a unified approach to interpreting model predictions"
    - "emotion intensities in tweets"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "from eliza to xiaoice challenges and opportunities with social chatbots"):
  with members:
    - "a persona based neural conversation model"
    - "towards empathetic human robot interactions"
    - "a network based end to end trainable task oriented dialogue system"
    - "stalematebreaker a proactive content introducing approach to automatic human computer conversation"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "wavenet a generative model for raw audio"
    - "achieving human parity in conversational speech recognition"
    - "senticap generating image descriptions with sentiments"

Connected through hyperedge HE-3 (induced by source paper "emotional chatting machine emotional conversation generation with internal and external memory"):
  with members:
    - "toward controlled generation of text"
    - "affect lm a neural language model for customizable affective text generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "topic aware neural response generation"
    - "key value memory networks for directly reading documents"
    - "neural responding machine for short text conversation"

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)