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 look around intelligently exploring unseen environments for unknown tasks"
  Abstract: It is common to implicitly assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is itself a major challenge. We address the problem of learning to look around: if a visual agent has the ability to voluntarily acquire new views to observe its environment, how can it learn efficient exploratory behaviors to acquire informative observations? We propose a reinforcement learning solution, where the agent is rewarded for actions that reduce its uncertainty about the unobserved portions of its environment. Based on this principle, we develop a recurrent neural network-based approach to perform active completion of panoramic natural scenes and 3D object shapes. Crucially, the learned policies are not tied to any recognition task nor to the particular semantic content seen during training. As a result, 1) the learned "look around" behavior is relevant even for new tasks in unseen environments, and 2) training data acquisition involves no manual labeling. Through tests in diverse settings, we demonstrate that our approach learns useful generic policies that transfer to new unseen tasks and environments. Completion episodes are shown at this https URL.

Connected through hyperedge HE-1 (induced by source paper "an exploration of embodied visual exploration"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "where to look next unsupervised active visual exploration on 360 input"
    - "embodied visual recognition"
    - "the replica dataset a digital replica of indoor spaces"
    - "emergence of exploratory look around behaviors through active observation completion"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for active human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "gibson env real world perception for embodied agents"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end recovery of human shape and pose"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-3 (induced by source paper "emergence of exploratory look around behaviors through active observation completion"):
  with members:
    - "learning exploration policies for navigation"
    - "learning real world robot policies by dreaming"
    - "sidekick policy learning for active visual exploration"
    - "semi parametric topological memory for navigation"
    - "world models"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-4 (induced by source paper "joint spatial and layer attention for convolutional networks"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "convolutional networks with adaptive inference graphs"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image based localization using lstms for structured feature correlation"

Connected through hyperedge HE-5 (induced by source paper "learning exploration policies for navigation"):
  with members:
    - "large scale study of curiosity driven learning"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "neural map structured memory for deep reinforcement learning"
    - "neural slam learning to explore with external memory"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "extreme relative pose network under hybrid representations"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "recurrent transformer networks for semantic correspondence"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "extreme relative pose estimation for rgb d scans via scene completion"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "unsupervised learning of depth and ego motion from video"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "revisiting deep architectures for head motion prediction in 360 videos"):
  with members:
    - "spherical clustering of users navigating 360 degree content"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "rate utility optimized streaming of volumetric media for augmented reality"
    - "predicting deeper into the future of semantic segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"

Connected through hyperedge HE-8 (induced by source paper "where to look next unsupervised active visual exploration on 360 input"):
  with members:
    - "emergence of exploratory look around behaviors through active observation completion"
    - "generative image inpainting with contextual attention"
    - "neural map structured memory for deep reinforcement learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "show attend and tell neural image caption generation with visual attention"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)