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 methods for conditional multimodal deep learning"
  Abstract: In this paper, we address the problem of conditional modality learning, whereby one is interested in generating one modality given the other. While it is straightforward to learn a joint distribution over multiple modalities using a deep multimodal architecture, we observe that such models aren't very effective at conditional generation. Hence, we address the problem by learning conditional distributions between the modalities. We use variational methods for maximizing the corresponding conditional log-likelihood. The resultant deep model, which we refer to as conditional multimodal autoencoder (CMMA), forces the latent representation obtained from a single modality alone to be `close' to the joint representation obtained from multiple modalities. We use the proposed model to generate faces from attributes. We show that the faces generated from attributes using the proposed model, are qualitatively and quantitatively more representative of the attributes from which they were generated, than those obtained by other deep generative models. We also propose a secondary task, whereby the existing faces are modified by modifying the corresponding attributes. We observe that the modifications in face introduced by the proposed model are representative of the corresponding modifications in attributes.

Connected through hyperedge HE-1 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "neuro serket development of integrative cognitive system through the composition of deep probabilistic generative models"):
  with members:
    - "cross modal variational auto encoder with distributed latent spaces and associators"
    - "multimodal generative models for scalable weakly supervised learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "serket an architecture for connecting stochastic models to realize a large scale cognitive model"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "nonparametric bayesian double articulation analyzer for direct language acquisition from continuous speech signals"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-4 (induced by source paper "disentangling latent hands for image synthesis and pose estimation"):
  with members:
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "dgpose disentangled semi supervised deep generative models for human body analysis"
    - "cross modal deep variational hand pose estimation"
    - "model based hand pose estimation for generalized hand shape with appearance normalization"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"

Connected through hyperedge HE-5 (induced by source paper "found in translation learning robust joint representations by cyclic translations between modalities"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal language analysis with recurrent multistage fusion"
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multimodal local global ranking fusion for emotion recognition"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "phrase based neural unsupervised machine translation"
    - "learning factorized multimodal representations"

Connected through hyperedge HE-6 (induced by source paper "multimodal generative models for scalable weakly supervised learning"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "deep variational canonical correlation analysis"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "unsupervised neural machine translation"
    - "towards deeper understanding of variational autoencoding models"
    - "generative models of visually grounded imagination"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multi attention recurrent network for human communication comprehension"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "triangle generative adversarial networks"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "triple generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "dense supervision for visual comparisons via synthetic images"):
  with members:
    - "variational methods for conditional multimodal learning generating human faces from attributes"
    - "convolutional network for attribute driven and identity preserving human face generation"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "attribute2image conditional image generation from visual attributes"
    - "deep relative attributes"
    - "spatial transformer networks"
    - "draw a recurrent neural network for image generation"
    - "synthetic data and artificial neural networks for natural scene text recognition"

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.CV(Computer Vision and Pattern Recognition)