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: "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
  Abstract: Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many distractors, often in restricted spaces. To tackle these challenges, we developed a deep-learning approach that combines object detection and semantic segmentation. The manipulation scenes are captured with RGB-D cameras, for which we developed a depth fusion method. Employing pretrained features makes learning from small annotated robotic datasets possible. We evaluate our approach on two challenging datasets: one captured for the Amazon Picking Challenge 2016, where our team NimbRo came in second in the Stowing and third in the Picking task; and one captured in disaster-response scenarios. The experiments show that object detection and semantic segmentation complement each other and can be combined to yield reliable object perception.

Connected through hyperedge HE-1 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands"):
  with members:
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "scene level pose estimation for multiple instances of densely packed objects"
    - "learning joint reconstruction of hands and manipulated objects"
    - "manipulation by feel touch based control with deep predictive models"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "principal components of touch"

Connected through hyperedge HE-2 (induced by source paper "reinforced active learning for image segmentation"):
  with members:
    - "multi target embodied question answering"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "panoptic feature pyramid networks"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "learning a policy for opportunistic active learning"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "discovering general purpose active learning strategies"
    - "dsnet for real time driving scene semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "the rgb d triathlon towards agile visual toolboxes for robots"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "3d pose regression using convolutional neural networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "straight to shapes real time instance segmentation made more accurate"):
  with members:
    - "vision meets drones a challenge"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "bottom up instance segmentation using deep higher order crfs"
    - "boundary aware instance segmentation"
    - "yolo9000 better faster stronger"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "value iteration networks on multiple levels of abstraction"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "gated path planning networks"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning generalized reactive policies using deep neural networks"
    - "generalized value iteration networks life beyond lattices"
    - "anytime hybrid driving stepping locomotion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-6 (induced by source paper "deep workpiece region segmentation for bin picking"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "understanding convolution for semantic segmentation"
    - "mask r cnn"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "robust grasp planning over uncertain shape completions"):
  with members:
    - "multi modal geometric learning for grasping and manipulation"
    - "transferring grasping skills to novel instances by latent space non rigid registration"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "shape completion enabled robotic grasping"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "lidar assisted large scale privacy protection in street view cycloramas"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "r fcn object detection via region based fully convolutional networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

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)