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: "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
  Abstract: We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic view of an indoor scene when given only a partial observation (<= 50%) in the form of an RGB-D image. To make this possible, Im2Pano3D leverages strong contextual priors learned from large-scale synthetic and real-world indoor scenes. To ease the prediction of 3D structure, we propose to parameterize 3D surfaces with their plane equations and train the model to predict these parameters directly. To provide meaningful training supervision, we use multiple loss functions that consider both pixel level accuracy and global context consistency. Experiments demon- strate that Im2Pano3D is able to predict the semantics and 3D structure of the unobserved scene with more than 56% pixel accuracy and less than 0.52m average distance error, which is significantly better than alternative approaches.

Connected through hyperedge HE-1 (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-2 (induced by source paper "spherical cnns on unstructured grids"):
  with members:
    - "deep neural networks motivated by partial differential equations"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning so 3 equivariant representations with spherical cnns"
    - "geometric deep learning going beyond euclidean data"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-3 (induced by source paper "grasping in the wild learning 6dof closed loop grasping from low cost demonstrations"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-4 (induced by source paper "deeplight learning illumination for unconstrained mobile mixed reality"):
  with members:
    - "learning to estimate indoor lighting from 3d objects"
    - "generative image inpainting with contextual attention"
    - "learning high dynamic range from outdoor panoramas"
    - "learning to predict indoor illumination from a single image"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "joint material and illumination estimation from photo sets in the wild"

Connected through hyperedge HE-5 (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"
    - "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"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-6 (induced by source paper "thinking outside the box generation of unconstrained 3d room layouts"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "weakly supervised object localization using things and stuff transfer"
    - "roomnet end to end room layout estimation"
    - "surface normals in the wild"
    - "mask r cnn"
    - "stuffnet using stuff to improve object detection"
    - "deeper depth prediction with fully convolutional residual networks"
    - "pano2cad room layout from a single panorama image"

Connected through hyperedge HE-7 (induced by source paper "neural illumination lighting prediction for indoor environments"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "geometry aware deep network for single image novel view synthesis"
    - "learning to estimate indoor lighting from 3d objects"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "learning to predict indoor illumination from a single image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "what is around the camera"

Connected through hyperedge HE-8 (induced by source paper "semantic segmentation of panoramic images using a synthetic dataset"):
  with members:
    - "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "unsupervised learning of depth and ego motion from panoramic video"
    - "importance aware semantic segmentation with efficient pyramidal context network for navigational assistant systems"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "adapting semantic segmentation models for changes in illumination and camera perspective"
    - "a comparative study of high recall real time semantic segmentation based on swift factorized network"

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)