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: "semantic segmentation from limited training data"
  Abstract: We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of the 2017 competition was the introduction of unseen categories. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored. One is a deep metric learning approach that works in three separate steps: semantic-agnostic boundary detection, patch classification and pixel-wise voting. The other is a fully-supervised semantic segmentation approach with efficient dataset collection. We conduct an extensive analysis of the two methods on our ARC 2017 dataset. Interestingly, only few examples of each class are sufficient to fine-tune even very deep convolutional neural networks for this specific task.

Connected through hyperedge HE-1 (induced by source paper "a novel inspection system for variable data printing using deep learning"):
  with members:
    - "anonet weakly supervised anomaly detection in textured surfaces"
    - "slum segmentation and change detection a deep learning approach"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning"
    - "spot the difference by object detection"
    - "fully convolutional siamese networks for change detection"
    - "light weight refinenet for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "video object segmentation based visual servo control and object depth estimation on a mobile robot platform"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "self supervised robot in hand object learning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "inferring occluded geometry improves performance when retrieving an object from dense clutter"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "mask r cnn"
    - "semantic scene completion from a single depth image"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-4 (induced by source paper "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"):
  with members:
    - "clusternet instance segmentation in rgb d images"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "end to end learning of semantic grasping"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "dense object nets learning dense visual object descriptors by and for robotic manipulation"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"

Connected through hyperedge HE-6 (induced by source paper "bayesian semantic instance segmentation in open set world"):
  with members:
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "semantic instance segmentation via deep metric learning"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"
    - "deep watershed transform for instance segmentation"
    - "end to end instance segmentation with recurrent attention"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "cartman the low cost cartesian manipulator that won the amazon robotics challenge"):
  with members:
    - "autonomous sweet pepper harvesting for protected cropping systems"
    - "mechanical design of a cartesian manipulator for warehouse pick and place"
    - "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"
    - "analysis and observations from the first amazon picking challenge"
    - "lessons from the amazon picking challenge"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

Connected through hyperedge HE-8 (induced by source paper "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"):
  with members:
    - "mechanical design of a cartesian manipulator for warehouse pick and place"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "a summary of team mit s approach to the amazon picking challenge 2015"

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)