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: "segicp integrated deep semantic segmentation and pose estimation"
  Abstract: Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1 cm position error and < 5° angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.

Connected through hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration"):
  with members:
    - "did you miss the sign a false negative alarm system for traffic sign detectors"
    - "consensus maximization tree search revisited"
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "a practical maximum clique algorithm for matching with pairwise constraints"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "prnet self supervised learning for partial to partial registration"
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"

Connected through hyperedge HE-2 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pixor real time 3d object detection from point clouds"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "bop benchmark for 6d object pose estimation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"

Connected through hyperedge HE-3 (induced by source paper "cross view semantic segmentation for sensing surroundings"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "high resolution representations for labeling pixels and regions"
    - "learning exploration policies for navigation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross view image synthesis using conditional gans"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "visual memory for robust path following"

Connected through hyperedge HE-4 (induced by source paper "self supervised 6d object pose estimation for robot manipulation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning instance segmentation by interaction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (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-6 (induced by source paper "deepicp an end to end deep neural network for 3d point cloud registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "learning depth with convolutional spatial propagation network"
    - "deeptam deep tracking and mapping"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "imls slam scan to model matching based on 3d data"

Connected through hyperedge HE-7 (induced by source paper "fruit detection segmentation and 3d visualisation of environments in apple orchards"):
  with members:
    - "single pixel reconstruction for one stage instance segmentation"
    - "yolov3 an incremental improvement"
    - "deep learning in agriculture a survey"
    - "focal loss for dense object detection"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "deep fruit detection in orchards"

Connected through hyperedge HE-8 (induced by source paper "a polynomial time solution for robust registration with extreme outlier rates"):
  with members:
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "rfm slam exploiting relative feature measurements to separate orientation and position estimation in slam"
    - "gogma globally optimal gaussian mixture alignment"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "fast algorithms for the maximum clique problem on massive graphs with applications to overlapping community detection"

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.RO(Robotics)