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: "learning models for visual 3d localization with implicit mapping"
  Abstract: We propose a formulation of visual localization that does not require construction of explicit maps in the form of point clouds or voxels. The goal is to learn an implicit representation of the environment at a higher, more abstract level, for instance that of objects. To study this approach we consider procedurally generated Minecraft worlds, for which we can generate visually rich images along with camera pose coordinates. We first show that Generative Query Networks (GQNs) enhanced with a novel attention mechanism can capture the visual structure of 3D scenes in Minecraft, as evidenced by their samples. We then apply the models to the localization problem, investigating both generative and discriminative approaches, and compare the different ways in which they each capture task uncertainty. Our results show that models with implicit mapping are able to capture the underlying 3D structure of visually complex scenes, and use this to accurately localize new observations, paving the way towards future applications in sequential localization. Supplementary video available at this https URL

Connected through hyperedge HE-1 (induced by source paper "geometry aware neural rendering"):
  with members:
    - "consistent generative query networks"
    - "learning dexterous in hand manipulation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "unifying map and landmark based representations for visual navigation"
    - "non local neural networks"
    - "attention is all you need"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-2 (induced by source paper "a generative map for image based camera localization"):
  with members:
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "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-3 (induced by source paper "attentive neural processes"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "image transformer"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "vfunc a deep generative model for functions"
    - "variational memory addressing in generative models"
    - "attention is all you need"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "neural multisensory scene inference"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "attentive neural processes"
    - "consistent generative query networks"
    - "federated learning strategies for improving communication efficiency"
    - "conditional neural processes"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "multimodal generative models for scalable weakly supervised learning"
    - "occupancy networks learning 3d reconstruction in function space"

Connected through hyperedge HE-5 (induced by source paper "sequential neural processes"):
  with members:
    - "attentive neural processes"
    - "state space lstm models with particle mcmc inference"
    - "learning latent dynamics for planning from pixels"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "learning and querying fast generative models for reinforcement learning"
    - "generative temporal models with memory"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "incremental scene synthesis"):
  with members:
    - "global pose estimation with an attention based recurrent network"
    - "gibson env real world perception for embodied agents"
    - "spectral normalization for generative adversarial networks"
    - "active neural localization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep view morphing"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

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)