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 autoencoded regression high dimensional regression of visual data on complex manifold"
  Abstract: This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional image responses, which is not handled by existing regression algorithms, is proposed. (ii) The proposed regression method introduces a strategy to learn the latent space as well as the encoder and decoder so that the result of the regressed response in the latent space coincide with the corresponding response in the data space. (iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework. We demonstrate the robustness and effectiveness of our method through a number of experiments on various visual data regression problems.

Connected through hyperedge HE-1 (induced by source paper "a generative map for image based camera localization"):
  with members:
    - "learning models for visual 3d localization with implicit mapping"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "deep variational canonical correlation analysis"
    - "image based localization using hourglass networks"
    - "geometry aware learning of maps for camera localization"
    - "generative models of visually grounded imagination"
    - "image based localization using lstms for structured feature correlation"

Connected through hyperedge HE-2 (induced by source paper "neural approximation of an auto regressive process through confidence guided sampling"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "mask predict parallel decoding of conditional masked language models"
    - "non monotonic sequential text generation"
    - "attentive neural processes"
    - "conditional neural processes"
    - "blockwise parallel decoding for deep autoregressive models"
    - "fast generation for convolutional autoregressive models"
    - "neural speed reading via skim rnn"

Connected through hyperedge HE-3 (induced by source paper "cross modal variational auto encoder with distributed latent spaces and associators"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "cross modal deep variational hand pose estimation"
    - "multimodal generative models for scalable weakly supervised learning"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "conditional generation of multi modal data using constrained embedding space mapping"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-4 (induced by source paper "variational autoencoder for regression application to brain aging analysis"):
  with members:
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"

Connected through hyperedge HE-5 (induced by source paper "traversing latent space using decision ferns"):
  with members:
    - "dual motion gan for future flow embedded video prediction"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "learning a driving simulator"
    - "adversarial feature learning"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep convolutional inverse graphics network"

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)