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 to linearize under uncertainty"
  Abstract: Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. However, a principled way in which to train such hierarchies in the unsupervised setting has remained elusive. In this work we suggest a new architecture and loss for training deep feature hierarchies that linearize the transformations observed in unlabeled natural video sequences. This is done by training a generative model to predict video frames. We also address the problem of inherent uncertainty in prediction by introducing latent variables that are non-deterministic functions of the input into the network architecture.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised disentanglement without compromises"):
  with members:
    - "disentanglement by nonlinear ica with general incompressible flow networks gin"
    - "weakly supervised disentanglement with guarantees"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "unsupervised model selection for variational disentangled representation learning"
    - "disentangling factors of variation using few labels"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"
    - "causality for machine learning"

Connected through hyperedge HE-2 (induced by source paper "adacof adaptive collaboration of flows for video frame interpolation"):
  with members:
    - "depth aware video frame interpolation"
    - "phasenet for video frame interpolation"
    - "the perception distortion tradeoff"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unsupervised learning of disentangled representations from video"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-3 (induced by source paper "pose augmentation class agnostic object pose transformation for object recognition"):
  with members:
    - "collagan collaborative gan for missing image data imputation"
    - "a style based generator architecture for generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "are disentangled representations helpful for abstract visual reasoning"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "on the fairness of disentangled representations"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"

Connected through hyperedge HE-5 (induced by source paper "on the fairness of disentangled representations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on variational bounds of mutual information"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-6 (induced by source paper "are we done with object recognition the icub robot s perspective"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep image retrieval learning global representations for image search"
    - "improved deep learning of object category using pose information"
    - "the curious robot learning visual representations via physical interactions"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "a deep representation for depth images from synthetic data"
    - "learning to recognize objects by retaining other factors of variation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning of foreground object segmentation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "unsupervised representation learning by sorting sequences"
    - "segflow joint learning for video object segmentation and optical flow"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-8 (induced by source paper "disentangling factors of variation using few labels"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on the fairness of disentangled representations"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"

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)