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: "rubystar a non task oriented mixture model dialog system"
  Abstract: RubyStar is a dialog system designed to create "human-like" conversation by combining different response generation strategies. RubyStar conducts a non-task-oriented conversation on general topics by using an ensemble of rule-based, retrieval-based and generative methods. Topic detection, engagement monitoring, and context tracking are used for managing interaction. Predictable elements of conversation, such as the bot's backstory and simple question answering are handled by separate modules. We describe a rating scheme we developed for evaluating response generation. We find that character-level RNN is an effective generation model for general responses, with proper parameter settings; however other kinds of conversation topics might benefit from using other models.

Connected through hyperedge HE-1 (induced by source paper "improving neural conversational models with entropy based data filtering"):
  with members:
    - "deep learning based chatbot models"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "neural response ranking for social conversation a data efficient approach"
    - "automatic evaluation of neural personality based chatbots"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"

Connected through hyperedge HE-2 (induced by source paper "proposal towards a personalized knowledge powered self play based ensemble dialog system"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "improving neural conversational models with entropy based data filtering"
    - "deep learning based chatbot models"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "exploration by random network distillation"
    - "advancing the state of the art in open domain dialog systems through the alexa prize"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-3 (induced by source paper "advancing the state of the art in open domain dialog systems through the alexa prize"):
  with members:
    - "slugbot developing a computational model andframework of a novel dialogue genre"
    - "achieving human parity on automatic chinese to english news translation"
    - "contextual language model adaptation for conversational agents"
    - "alquist the alexa prize socialbot"
    - "topic based evaluation for conversational bots"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "tartan a retrieval based socialbot powered by a dynamic finite state machine architecture"
    - "conversational ai the science behind the alexa prize"

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"
    - "a practical approach to dialogue response generation in closed domains"
    - "edina building an open domain socialbot with self dialogues"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "efficient purely convolutional text encoding"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "axiomatic attribution for deep networks"
    - "convolutional sequence to sequence learning"
    - "instance normalization the missing ingredient for fast stylization"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "conversational ai the science behind the alexa prize"):
  with members:
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "predicting causes of reformulation in intelligent assistants"
    - "opennmt open source toolkit for neural machine translation"
    - "lstm based conversation models"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "a neural conversational model"

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)