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: "bingham procrustean alignment for object detection in clutter"
  Abstract: A new system for object detection in cluttered RGB-D images is presented. Our main contribution is a new method called Bingham Procrustean Alignment (BPA) to align models with the scene. BPA uses point correspondences between oriented features to derive a probability distribution over possible model poses. The orientation component of this distribution, conditioned on the position, is shown to be a Bingham distribution. This result also applies to the classic problem of least-squares alignment of point sets, when point features are orientation-less, and gives a principled, probabilistic way to measure pose uncertainty in the rigid alignment problem. Our detection system leverages BPA to achieve more reliable object detections in clutter.

Connected through hyperedge HE-1 (induced by source paper "grasp pose detection in point clouds"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep learning for detecting robotic grasps"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "open world assistive grasping using laser selection"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "real time grasp detection using convolutional neural networks"
    - "a framework for the volumetric integration of depth images"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-3 (induced by source paper "robot in a room toward perfect object recognition in closed environments"):
  with members:
    - "learning rich features from rgb d images for object detection and segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "perch perception via search for multi object recognition and localization"):
  with members:
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-5 (induced by source paper "using geometry to detect grasps in 3d point clouds"):
  with members:
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-6 (induced by source paper "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"):
  with members:
    - "deep learning for detecting robotic grasps"

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)