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: "multiresolution recurrent neural networks an application to dialogue response generation"
  Abstract: We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.

Connected through hyperedge HE-1 (induced by source paper "avgout a simple output probability measure to eliminate dull responses"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "improving abstraction in text summarization"
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "polite dialogue generation without parallel data"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "learning to decode for future success"
    - "generating high quality and informative conversation responses with sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "a study of reinforcement learning for neural machine translation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "latent variable dialogue models and their diversity"

Connected through hyperedge HE-3 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "multiscale analysis for higher order tensors"
    - "generating long term trajectories using deep hierarchical networks"
    - "tensor regression meets gaussian processes"
    - "tensorly tensor learning in python"
    - "why should i trust you explaining the predictions of any classifier"
    - "parallel tensor compression for large scale scientific data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "mask focus conversation modelling by learning concepts"):
  with members:
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural conversational model"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-5 (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-6 (induced by source paper "survey on evaluation methods for dialogue systems"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "coqa a conversational question answering challenge"
    - "topic based evaluation for conversational bots"
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"
    - "quac question answering in context"
    - "a hybrid convolutional variational autoencoder for text generation"

Connected through hyperedge HE-7 (induced by source paper "dlgnet a transformer based model for dialogue response generation"):
  with members:
    - "an adversarial learning framework for a persona based multi turn dialogue model"
    - "the curious case of neural text degeneration"
    - "ernie enhanced representation through knowledge integration"
    - "adversarial bootstrapping for dialogue model training"
    - "defending against neural fake news"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-8 (induced by source paper "goal embedded dual hierarchical model for task oriented dialogue generation"):
  with members:
    - "a repository of conversational datasets"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "simple fast accurate intent classification and slot labeling"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "global locally self attentive dialogue state tracker"
    - "on the state of the art of evaluation in neural language models"

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)