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 conditional variational framework for dialog generation"
  Abstract: Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.

Connected through hyperedge HE-1 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "emma an emotionally intelligent personal assistant for improving wellbeing"
    - "finding good representations of emotions for text classification"
    - "sentiment adaptive end to end dialog systems"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "topic guided variational autoencoders for text generation"):
  with members:
    - "sequence generation with guider network"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "texygen a benchmarking platform for text generation models"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "selective encoding for abstractive sentence summarization"

Connected through hyperedge HE-5 (induced by source paper "conditional single view shape generation for multi view stereo reconstruction"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "recurrent transformer networks for semantic correspondence"
    - "so net self organizing network for point cloud analysis"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "learning to reconstruct shapes from unseen classes"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-6 (induced by source paper "diversifying topic coherent response generation for natural multi turn conversations"):
  with members:
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "multi level memory for task oriented dialogs"
    - "variational memory encoder decoder"
    - "language gans falling short"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "diverse beam search for increased novelty in abstractive summarization"
    - "toward controlled generation of text"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-7 (induced by source paper "towards generating long and coherent text with multi level latent variable models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "topic guided variational autoencoders for text generation"
    - "variational sequential labelers for semi supervised learning"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-8 (induced by source paper "retrieval based goal oriented dialogue generation"):
  with members:
    - "scaling multi domain dialogue state tracking via query reformulation"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "on extractive and abstractive neural document summarization with transformer language models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "towards universal dialogue state tracking"
    - "a strong baseline for question relevancy ranking"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"

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)