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: "probabilistic 3d multilabel real time mapping for multi object manipulation"
  Abstract: Probabilistic 3D map has been applied to object segmentation with multiple camera viewpoints, however, conventional methods lack of real-time efficiency and functionality of multilabel object mapping. In this paper, we propose a method to generate three-dimensional map with multilabel occupancy in real-time. Extending our previous work in which only target label occupancy is mapped, we achieve multilabel object segmentation in a single looking around action. We evaluate our method by testing segmentation accuracy with 39 different objects, and applying it to a manipulation task of multiple objects in the experiments. Our mapping-based method outperforms the conventional projection-based method by 40 - 96\% relative (12.6 mean $IU_{3d}$), and robot successfully recognizes (86.9\%) and manipulates multiple objects (60.7\%) in an environment with heavy occlusions.

Connected through hyperedge HE-1 (induced by source paper "joint learning of instance and semantic segmentation for robotic pick and place with heavy occlusions in clutter"):
  with members:
    - "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "joint learning of intrinsic images and semantic segmentation"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "segflow joint learning for video object segmentation and optical flow"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"):
  with members:
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "on pre trained image features and synthetic images for deep learning"
    - "synthesizing training data for object detection in indoor scenes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning from simulated and unsupervised images through adversarial training"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "learning to refine object segments"

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)