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: "consistent generative query networks"
  Abstract: Stochastic video prediction is usually framed as an extrapolation problem where the goal is to sample a sequence of consecutive future image frames conditioned on a sequence of observed past frames. For the most part, algorithms for this task generate future video frames sequentially in an autoregressive fashion, which is slow and requires the input and output to be consecutive. We introduce a model that overcomes these drawbacks -- it learns to generate a global latent representation from an arbitrary set of frames within a video. This representation can then be used to simultaneously and efficiently sample any number of temporally consistent frames at arbitrary time-points in the video. We apply our model to synthetic video prediction tasks and achieve results that are comparable to state-of-the-art video prediction models. In addition, we demonstrate the flexibility of our model by applying it to 3D scene reconstruction where we condition on location instead of time. To the best of our knowledge, our model is the first to provide flexible and coherent prediction on stochastic video datasets, as well as consistent 3D scene samples. Please check the project website this https URL to view scene reconstructions and videos produced by our model.

Connected through hyperedge HE-1 (induced by source paper "geometry aware neural rendering"):
  with members:
    - "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"
    - "learning models for visual 3d localization with implicit mapping"
    - "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 "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "attentive neural processes"
    - "the functional neural process"
    - "neural processes"
    - "conditional neural processes"
    - "structural recurrent neural network srnn for group activity analysis"
    - "attention is all you need"
    - "the statistical recurrent unit"

Connected through hyperedge HE-3 (induced by source paper "on the limitations of representing functions on sets"):
  with members:
    - "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
    - "attentive neural processes"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "attention is all you need"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-4 (induced by source paper "attentive neural processes"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "image transformer"
    - "conditional neural processes"
    - "vfunc a deep generative model for functions"
    - "learning models for visual 3d localization with implicit mapping"
    - "variational memory addressing in generative models"
    - "attention is all you need"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "neural multisensory scene inference"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "attentive neural processes"
    - "federated learning strategies for improving communication efficiency"
    - "conditional neural processes"
    - "learning models for visual 3d localization with implicit mapping"
    - "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-6 (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"
    - "conditional neural processes"
    - "learning and querying fast generative models for reinforcement learning"
    - "learning models for visual 3d localization with implicit mapping"
    - "generative temporal models with memory"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "differentiable disentanglement filter an application agnostic core concept discovery probe"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "spine sparse interpretable neural embeddings"
    - "grounded language learning in a simulated 3d world"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "neural machine translation by jointly learning to align and translate"

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)