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: "variational memory encoder decoder"
  Abstract: Introducing variability while maintaining coherence is a core task in learning to generate utterances in conversation. Standard neural encoder-decoder models and their extensions using conditional variational autoencoder often result in either trivial or digressive responses. To overcome this, we explore a novel approach that injects variability into neural encoder-decoder via the use of external memory as a mixture model, namely Variational Memory Encoder-Decoder (VMED). By associating each memory read with a mode in the latent mixture distribution at each timestep, our model can capture the variability observed in sequential data such as natural conversations. We empirically compare the proposed model against other recent approaches on various conversational datasets. The results show that VMED consistently achieves significant improvement over others in both metric-based and qualitative evaluations.

Connected through hyperedge HE-1 (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"
    - "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"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-2 (induced by source paper "learning to reason with relational video representation for question answering"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "progressive attention memory network for movie story question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "motion appearance co memory networks for video question answering"
    - "multimodal dual attention memory for video story question answering"
    - "compositional attention networks for machine reasoning"
    - "tvqa localized compositional video question answering"
    - "movie question answering remembering the textual cues for layered visual contents"

Connected through hyperedge HE-3 (induced by source paper "learning from fact checkers analysis and generation of fact checking language"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "fact checking in community forums"
    - "automated hate speech detection and the problem of offensive language"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"

Connected through hyperedge HE-4 (induced by source paper "knowledge grounded response generation with deep attentional latent variable model"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "understanding convolutional neural networks for text classification"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "a knowledge grounded neural conversation model"
    - "hierarchical recurrent attention network for response generation"

Connected through hyperedge HE-5 (induced by source paper "relational dynamic memory networks"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "attentional multilabel learning over graphs a message passing approach"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "compositional attention networks for machine reasoning"
    - "a deep tree based model for software defect prediction"
    - "learning deep matrix representations"

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)