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 practical approach to dialogue response generation in closed domains"
  Abstract: We describe a prototype dialogue response generation model for the customer service domain at Amazon. The model, which is trained in a weakly supervised fashion, measures the similarity between customer questions and agent answers using a dual encoder network, a Siamese-like neural network architecture. Answer templates are extracted from embeddings derived from past agent answers, without turn-by-turn annotations. Responses to customer inquiries are generated by selecting the best template from the final set of templates. We show that, in a closed domain like customer service, the selected templates cover $>$70\% of past customer inquiries. Furthermore, the relevance of the model-selected templates is significantly higher than templates selected by a standard tf-idf baseline.

Connected through hyperedge HE-1 (induced by source paper "building a production model for retrieval based chatbots"):
  with members:
    - "diversifying reply suggestions using a matching conditional variational autoencoder"
    - "a hybrid retrieval generation neural conversation model"
    - "neural response ranking for social conversation a data efficient approach"
    - "an efficient framework for learning sentence representations"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "automatic quality estimation for natural language generation ranting jointly rating and ranking"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "reference less quality estimation of text simplification systems"
    - "end to end content and plan selection for data to text generation"
    - "referenceless quality estimation for natural language generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"
    - "challenges in data to document generation"
    - "adversarial evaluation of dialogue models"

Connected through hyperedge HE-3 (induced by source paper "translator2vec understanding and representing human post editors"):
  with members:
    - "recurrent neural network based user authentication for freely typed keystroke data"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "learning to represent edits"
    - "marian fast neural machine translation in c"
    - "attention is all you need"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "bag of tricks for efficient text classification"
    - "a convolutional encoder model for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural response ranking for social conversation a data efficient approach"):
  with members:
    - "avoiding echo responses in a retrieval based conversation system"
    - "billion scale similarity search with gpus"
    - "edina building an open domain socialbot with self dialogues"
    - "rubystar a non task oriented mixture model dialog system"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "an ensemble model with ranking for social dialogue"
    - "deep reinforcement learning for dialogue generation"
    - "conversational contextual cues the case of personalization and history for response ranking"

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)