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: "poseagent budget constrained 6d object pose estimation via reinforcement learning"
  Abstract: State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.

Connected through hyperedge HE-1 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-2 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "real time object pose estimation with pose interpreter networks"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "physics based scene level reasoning for object pose estimation in clutter"

Connected through hyperedge HE-3 (induced by source paper "verefine integrating object pose verification with iterative physics guided refinement"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "graspa 1 0 graspa is a robot arm grasping performance benchmark"
    - "dpod 6d pose object detector and refiner"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"

Connected through hyperedge HE-4 (induced by source paper "deep 6dpose recovering 6d object pose from a single rgb image"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "global hypothesis generation for 6d object pose estimation"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "ffnet video fast forwarding via reinforcement learning"):
  with members:
    - "egosampling wide view hyperlapse from egocentric videos"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "collaborative summarization of topic related videos"
    - "towards semantic fast forward and stabilized egocentric videos"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"

Connected through hyperedge HE-6 (induced by source paper "deep 6 dof tracking"):
  with members:
    - "6 dof object pose from semantic keypoints"
    - "training a feedback loop for hand pose estimation"
    - "deep image homography estimation"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "fully convolutional siamese networks for object tracking"
    - "siamese regression networks with efficient mid level feature extraction for 3d object pose estimation"
    - "you only look once unified real time object detection"
    - "first step toward model free anonymous object tracking with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "facial 3d model registration under occlusions with sensiblepoints based reinforced hypothesis refinement"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "a convolution tree with deconvolution branches exploiting geometric relationships for single shot keypoint detection"
    - "do we really need to collect millions of faces for effective face recognition"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "pooling faces template based face recognition with pooled face images"
    - "learning analysis by synthesis for 6d pose estimation in rgb d images"
    - "adam a method for stochastic optimization"

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)