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: "a summary of team mit s approach to the amazon picking challenge 2015"
  Abstract: The Amazon Picking Challenge (APC), held alongside the International Conference on Robotics and Automation in May 2015 in Seattle, challenged roboticists from academia and industry to demonstrate fully automated solutions to the problem of picking objects from shelves in a warehouse fulfillment scenario. Packing density, object variability, speed, and reliability are the main complexities of the task. The picking challenge serves both as a motivation and an instrument to focus research efforts on a specific manipulation problem. In this document, we describe Team MIT's approach to the competition, including design considerations, contributions, and performance, and we compile the lessons learned. We also describe what we think are the main remaining challenges.

Connected through hyperedge HE-1 (induced by source paper "development of a robotic system for automated decaking of 3d printed parts"):
  with members:
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "analysis and observations from the first amazon picking challenge"
    - "lessons from the amazon picking challenge"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "photorealistic image synthesis for object instance detection"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "modeling visual context is key to augmenting object detection datasets"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "cut paste and learn surprisingly easy synthesis for instance detection"

Connected through hyperedge HE-3 (induced by source paper "vision based picking system for automatic express package dispatching"):
  with members:
    - "grasp pose detection in point clouds"
    - "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "high precision grasp pose detection in dense clutter"
    - "yolo9000 better faster stronger"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

Connected through hyperedge HE-4 (induced by source paper "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"):
  with members:
    - "nimbro rescue solving disaster response tasks through mobile manipulation robot momaro"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "iterative visual recognition for learning based randomized bin picking"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "densecap fully convolutional localization networks for dense captioning"

Connected through hyperedge HE-5 (induced by source paper "design and development of an automated robotic pick stow system for an e commerce warehouse"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "ssd single shot multibox detector"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "continuous control with deep reinforcement learning"
    - "fast r cnn"
    - "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"

Connected through hyperedge HE-6 (induced by source paper "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"):
  with members:
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "spatial transformer networks"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-7 (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"
    - "semantic segmentation from limited training data"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-8 (induced by source paper "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"):
  with members:
    - "dorapicker an autonomous picking system for general objects"
    - "analysis and observations from the first amazon picking challenge"
    - "3dmatch learning the matching of local 3d geometry in range scans"
    - "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "fully convolutional networks for semantic segmentation"

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)