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: "attention with intention for a neural network conversation model"
  Abstract: In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-to-end without labeling data. Experiments show that this model generates natural responses to user inputs.

Connected through hyperedge HE-1 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-2 (induced by source paper "multimodal transformer networks for end to end video grounded dialogue systems"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "generating wikipedia by summarizing long sequences"
    - "training millions of personalized dialogue agents"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "jointly localizing and describing events for dense video captioning"

Connected through hyperedge HE-3 (induced by source paper "augmenting non collaborative dialog systems with explicit semantic and strategic dialog history"):
  with members:
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "unsupervised dialog structure learning"
    - "a dynamic strategy coach for effective negotiation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "tartan a retrieval based socialbot powered by a dynamic finite state machine architecture"
    - "sounding board a user centric and content driven social chatbot"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"

Connected through hyperedge HE-4 (induced by source paper "sentnet source aware recurrent entity network for dialogue response selection"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "a persona based neural conversation model"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

Connected through hyperedge HE-5 (induced by source paper "teacher student framework enhanced multi domain dialogue generation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "flexible and scalable state tracking framework for goal oriented dialogue systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "toward scalable neural dialogue state tracking model"
    - "scalable multi domain dialogue state tracking"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural belief tracker data driven dialogue state tracking"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-6 (induced by source paper "a dataset for building code mixed goal oriented conversation systems"):
  with members:
    - "part of speech tagging for code switched data"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "a persona based neural conversation model"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-7 (induced by source paper "generating descriptions from structured data using a bifocal attention mechanism and gated orthogonalization"):
  with members:
    - "diversity driven attention model for query based abstractive summarization"
    - "neural text generation from structured data with application to the biography domain"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "sequence to sequence rnns for text summarization"
    - "a neural attention model for abstractive sentence summarization"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "automatic evaluation of neural personality based chatbots"):
  with members:
    - "controlling linguistic style aspects in neural language generation"
    - "data distillation for controlling specificity in dialogue generation"
    - "multi task learning for speaker role adaptation in neural conversation models"
    - "a persona based neural conversation model"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "generative deep neural networks for dialogue a short review"
    - "a diversity promoting objective function for neural conversation models"
    - "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.NE(Neural and Evolutionary Computing)