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: "hashmod a hashing method for scalable 3d object detection"
  Abstract: We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashing techniques to match these views against the input frame in a scalable way. While a similar approach already exists for 2D detection, we show how to extend it to estimate the 3D pose of the detected objects. In particular, we explore different hashing strategies and identify the one which is more suitable to our problem. We show empirically that the complexity of our method is sublinear with the number of objects and we enable detection and pose estimation of many 3D objects with high accuracy while outperforming the state-of-the-art in terms of runtime.

Connected through hyperedge HE-1 (induced by source paper "one point one object simultaneous 3d object segmentation and 6 dof pose estimation"):
  with members:
    - "recognition of feature curves on 3d shapes using an algebraic approach to hough transforms"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "ssd single shot multibox detector"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "real time 6d object pose estimation on cpu"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "going further with point pair features"
    - "learning descriptors for object recognition and 3d pose estimation"

Connected through hyperedge HE-3 (induced by source paper "real time 3d model tracking in color and depth on a single cpu core"):
  with members:
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"

Connected through hyperedge HE-4 (induced by source paper "explaining the ambiguity of object detection and 6d pose from visual data"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep model based 6d pose refinement in rgb"
    - "pose estimation for objects with rotational symmetry"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "roi 10d monocular lifting of 2d detection to 6d pose and metric shape"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "wavelet based reflection symmetry detection via textural and color histograms"

Connected through hyperedge HE-5 (induced by source paper "occlusion resistant object rotation regression from point cloud segments"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "dynamic graph cnn for learning on point clouds"
    - "3d pose regression using convolutional neural networks"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-6 (induced by source paper "ipose instance aware 6d pose estimation of partly occluded objects"):
  with members:
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "mask r cnn"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "ssd single shot multibox detector"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deep supervision with shape concepts for occlusion aware 3d object parsing"

Connected through hyperedge HE-7 (induced by source paper "recovering 6d object pose a review and multi modal analysis"):
  with members:
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "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"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "iterative hough forest with histogram of control points for 6 dof object registration from depth images"
    - "analysis and observations from the first amazon picking challenge"

Connected through hyperedge HE-8 (induced by source paper "ssd 6d making rgb based 3d detection and 6d pose estimation great again"):
  with members:
    - "lift learned invariant feature transform"
    - "3d bounding box estimation using deep learning and geometry"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "fast single shot detection and pose estimation"
    - "6d object detection and next best view prediction in the crowd"

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)