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 syntactically constrained bidirectional asynchronous approach for emotional conversation generation"
  Abstract: Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (E-SCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines.

Connected through hyperedge HE-1 (induced by source paper "empgan multi resolution interactive empathetic dialogue generation"):
  with members:
    - "affect driven dialog generation"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"
    - "affect lm a neural language model for customizable affective text generation"

Connected through hyperedge HE-2 (induced by source paper "a simple dual decoder model for generating response with sentiment"):
  with members:
    - "sentiment adaptive end to end dialog systems"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "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"
    - "a diversity promoting objective function for neural conversation models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)