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: "fast robust continuous monocular egomotion computation"
  Abstract: We propose robust methods for estimating camera egomotion in noisy, real-world monocular image sequences in the general case of unknown observer rotation and translation with two views and a small baseline. This is a difficult problem because of the nonconvex cost function of the perspective camera motion equation and because of non-Gaussian noise arising from noisy optical flow estimates and scene non-rigidity. To address this problem, we introduce the expected residual likelihood method (ERL), which estimates confidence weights for noisy optical flow data using likelihood distributions of the residuals of the flow field under a range of counterfactual model parameters. We show that ERL is effective at identifying outliers and recovering appropriate confidence weights in many settings. We compare ERL to a novel formulation of the perspective camera motion equation using a lifted kernel, a recently proposed optimization framework for joint parameter and confidence weight estimation with good empirical properties. We incorporate these strategies into a motion estimation pipeline that avoids falling into local minima. We find that ERL outperforms the lifted kernel method and baseline monocular egomotion estimation strategies on the challenging KITTI dataset, while adding almost no runtime cost over baseline egomotion methods.

Connected through hyperedge HE-1 (induced by source paper "two body problem collaborative visual task completion"):
  with members:
    - "tarmac targeted multi agent communication"
    - "understanding grounded language learning agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"
    - "home a household multimodal environment"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "iqa visual question answering in interactive environments"):
  with members:
    - "yolov3 an incremental improvement"
    - "understanding grounded language learning agents"
    - "inferring and executing programs for visual reasoning"
    - "gated attention architectures for task oriented language grounding"
    - "learning to reason end to end module networks for visual question answering"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "visual semantic planning using deep successor representations"

Connected through hyperedge HE-3 (induced by source paper "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"):
  with members:
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "unsupervised learning of depth and ego motion from video"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised image to image translation with generative adversarial networks"
    - "sfm net learning of structure and motion from video"
    - "convolutional pose machines"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "understanding image motion with group representations"):
  with members:
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "unsupervised visual representation learning by context prediction"
    - "spatio temporal video autoencoder with differentiable memory"
    - "unsupervised learning of visual representations using videos"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning image representations tied to ego motion"

Connected through hyperedge HE-5 (induced by source paper "target driven visual navigation in indoor scenes using deep reinforcement learning"):
  with members:
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "asynchronous methods for deep reinforcement learning"
    - "building machines that learn and think like people"
    - "learning physical intuition of block towers by example"
    - "what happens if learning to predict the effect of forces in images"
    - "state of the art control of atari games using shallow reinforcement learning"
    - "newtonian image understanding unfolding the dynamics of objects in static images"

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)