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: "pose induction for novel object categories"
  Abstract: We address the task of predicting pose for objects of unannotated object categories from a small seed set of annotated object classes. We present a generalized classifier that can reliably induce pose given a single instance of a novel category. In case of availability of a large collection of novel instances, our approach then jointly reasons over all instances to improve the initial estimates. We empirically validate the various components of our algorithm and quantitatively show that our method produces reliable pose estimates. We also show qualitative results on a diverse set of classes and further demonstrate the applicability of our system for learning shape models of novel object classes.

Connected through hyperedge HE-1 (induced by source paper "few shot viewpoint estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "on first order meta learning algorithms"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "few shot segmentation propagation with guided networks"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "inferring 3d shapes from image collections using adversarial networks"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "deep shading convolutional neural networks for screen space shading"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "neural 3d mesh renderer"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-3 (induced by source paper "gp2c geometric projection parameter consensus for joint 3d pose and focal length estimation in the wild"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose estimation for fine grained object categories"
    - "a unified framework for multi view multi class object pose estimation"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-4 (induced by source paper "learning to estimate pose and shape of hand held objects from rgb images"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "global search with bernoulli alternation kernel for task oriented grasping informed by simulation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (induced by source paper "starmap for category agnostic keypoint and viewpoint estimation"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "extreme clicking for efficient object annotation"
    - "learning feature pyramids for human pose estimation"
    - "convolutional networks for object category and 3d pose estimation from 2d images"
    - "click here human localized keypoints as guidance for viewpoint estimation"

Connected through hyperedge HE-6 (induced by source paper "3d pose estimation and 3d model retrieval for objects in the wild"):
  with members:
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "6 dof object pose from semantic keypoints"
    - "3d bounding box estimation using deep learning and geometry"
    - "learning a predictable and generative vector representation for objects"
    - "identity mappings in deep residual networks"
    - "crafting a multi task cnn for viewpoint estimation"
    - "3d object class detection in the wild"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-7 (induced by source paper "a robot localization framework using cnns for object detection and pose estimation"):
  with members:
    - "the challenge of simultaneous object detection and pose estimation a comparative study"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "synthesizing training data for object detection in indoor scenes"

Connected through hyperedge HE-8 (induced by source paper "the challenge of simultaneous object detection and pose estimation a comparative study"):
  with members:
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "crafting a multi task cnn for viewpoint estimation"
    - "deep residual learning for image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "viewpoints and keypoints"
    - "caffe convolutional architecture for fast feature embedding"

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)