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 sequential matching framework for multi turn response selection in retrieval based chatbots"
  Abstract: We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. The analysis motivates us to propose a new matching framework that can sufficiently carry the important information in contexts to matching and model the relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interacts with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) which models the relationships among the utterances. The context-response matching is finally calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experimental results show that both models can significantly outperform the state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage the important information in contexts for matching.

Connected through hyperedge HE-1 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "co sampling training robust networks for extremely noisy supervision"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "fidelity weighted learning"
    - "a persona based neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "retrieval enhanced adversarial training for neural response generation"):
  with members:
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "opennmt open source toolkit for neural machine translation"
    - "adversarial learning for neural dialogue generation"
    - "adversarial ranking for language generation"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a network based end to end trainable task oriented dialogue system"

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)