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: "synthetically trained neural networks for learning human readable plans from real world demonstrations"
  Abstract: We present a system to infer and execute a human-readable program from a real-world demonstration. The system consists of a series of neural networks to perform perception, program generation, and program execution. Leveraging convolutional pose machines, the perception network reliably detects the bounding cuboids of objects in real images even when severely occluded, after training only on synthetic images using domain randomization. To increase the applicability of the perception network to new scenarios, the network is formulated to predict in image space rather than in world space. Additional networks detect relationships between objects, generate plans, and determine actions to reproduce a real-world demonstration. The networks are trained entirely in simulation, and the system is tested in the real world on the pick-and-place problem of stacking colored cubes using a Baxter robot.

Connected through hyperedge HE-1 (induced by source paper "camera to robot pose estimation from a single image"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "real time 3d traffic cone detection for autonomous driving"
    - "deep high resolution representation learning for human pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "few shot viewpoint estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "on first order meta learning algorithms"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "few shot segmentation propagation with guided networks"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "prototypical networks for few shot learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-3 (induced by source paper "prospection interpretable plans from language by predicting the future"):
  with members:
    - "universal planning networks"
    - "evaluating methods for end user creation of robot task plans"
    - "visual robot task planning"
    - "learning model based planning from scratch"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "simulating action dynamics with neural process networks"
    - "imagination augmented agents for deep reinforcement learning"
    - "learning interpretable spatial operations in a rich 3d blocks world"

Connected through hyperedge HE-4 (induced by source paper "visual geometric skill inference by watching human demonstration"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "online object and task learning via human robot interaction"
    - "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-5 (induced by source paper "sim to multi real transfer of low level robust control policies to multiple quadrotors"):
  with members:
    - "neuroflight next generation flight control firmware"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "sim to real learning agile locomotion for quadruped robots"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "reinforcement learning for pivoting task"
    - "control of a quadrotor with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"):
  with members:
    - "bridging cognitive programs and machine learning"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "deep learning a critical appraisal"
    - "reward learning from narrated demonstrations"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "inferring and executing programs for visual reasoning"
    - "star rt visual attention for real time video game playing"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-7 (induced by source paper "semantic mapping with simultaneous object detection and localization"):
  with members:
    - "multiple object detection tracking and long term dynamics learning in large 3d maps"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "semantic robot programming for goal directed manipulation in cluttered scenes"
    - "meaningful maps with object oriented semantic mapping"
    - "ssd single shot multibox detector"
    - "semanticfusion dense 3d semantic mapping with convolutional neural 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.RO(Robotics)