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: "analysis and observations from the first amazon picking challenge"
  Abstract: This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge.

Connected through hyperedge HE-1 (induced by source paper "robust 6d object pose estimation by learning rgb d features"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "explaining the ambiguity of object detection and 6d pose from visual data"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-2 (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-3 (induced by source paper "rigid soft interactive learning for robust grasping"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "the curious robot learning visual representations via physical interactions"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"

Connected through hyperedge HE-4 (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"
    - "a summary of team mit s approach to the amazon picking challenge 2015"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "lessons from the amazon picking challenge"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "koopman based control of a soft continuum manipulator under variable loading conditions"):
  with members:
    - "comparison and experimental validation of predictive models for soft fiber reinforced actuators"
    - "active learning of dynamics for data driven control using koopman operators"
    - "modeling and control of soft robots using the koopman operator and model predictive control"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "model based control using koopman operators"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"

Connected through hyperedge HE-6 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback"):
  with members:
    - "tactile mapping and localization from high resolution tactile imprints"
    - "manipulation by feel touch based control with deep predictive models"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "in hand object stabilization by independent finger control"
    - "in hand manipulation via motion cones"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "reactive planar manipulation with convex hybrid mpc"

Connected through hyperedge HE-7 (induced by source paper "overview and challenges of ambient systems towards a constructivist approach to their modelling"):
  with members:
    - "promises and challenges of ambient assisted living systems"

Connected through hyperedge HE-8 (induced by source paper "meta world a benchmark and evaluation for multi task and meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta learning by the baldwin effect"

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)