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: "neural emoji recommendation in dialogue systems"
  Abstract: Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smooth the communications between users, making dialogue systems more anthropomorphic and vivid. In this paper, we focus on automatically recommending appropriate emojis given the contextual information in multi-turn dialogue systems, where the challenges locate in understanding the whole conversations. More specifically, we propose the hierarchical long short-term memory model (H-LSTM) to construct dialogue representations, followed by a softmax classifier for emoji classification. We evaluate our models on the task of emoji classification in a real-world dataset, with some further explorations on parameter sensitivity and case study. Experimental results demonstrate that our method achieves the best performances on all evaluation metrics. It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.

Connected through hyperedge HE-1 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "understanding chat messages for sticker recommendation in hike messenger"
    - "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"
    - "product aware answer generation in e commerce question answering"
    - "how to write summaries with patterns learning towards abstractive summarization through prototype editing"
    - "abstractive text summarization by incorporating reader comments"
    - "think visually question answering through virtual imagery"
    - "multimodal emoji prediction"

Connected through hyperedge HE-2 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "emma an emotionally intelligent personal assistant for improving wellbeing"
    - "finding good representations of emotions for text classification"
    - "sentiment adaptive end to end dialog systems"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"

Connected through hyperedge HE-3 (induced by source paper "mojitalk generating emotional responses at scale"):
  with members:
    - "toward controlled generation of text"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "deep reinforcement learning for dialogue generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"

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)