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: "translating videos to commands for robotic manipulation with deep recurrent neural networks"
  Abstract: We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN.

Connected through hyperedge HE-1 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "combined task and action learning from human demonstrations for mobile manipulation applications"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "integrating knowledge and reasoning in image understanding"
    - "improving grounded natural language understanding through human robot dialog"
    - "learning actions from human demonstration video for robotic manipulation"
    - "prospection interpretable plans from language by predicting the future"

Connected through hyperedge HE-2 (induced by source paper "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"):
  with members:
    - "object captioning and retrieval with natural language"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "real time 6dof pose relocalization for event cameras with stacked spatial lstm networks"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-3 (induced by source paper "constructing dynamic knowledge graph for visual semantic understanding and applications in autonomous robotics"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "on exploring undetermined relationships for visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "pointing novel objects in image captioning"
    - "improving grounded natural language understanding through human robot dialog"
    - "context and attribute grounded dense captioning"
    - "connecting touch and vision via cross modal prediction"

Connected through hyperedge HE-4 (induced by source paper "tactile based insertion for dense box packing"):
  with members:
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"
    - "slip detection with combined tactile and visual information"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning robot activities from first person human videos using convolutional future regression"

Connected through hyperedge HE-5 (induced by source paper "robot learning and execution of collaborative manipulation plans from youtube videos"):
  with members:
    - "procedure planning in instructional videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "unsupervised learning and segmentation of complex activities from video"
    - "yolov3 an incremental improvement"
    - "functional object oriented network construction expansion"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "towards automatic learning of procedures from web instructional videos"

Connected through hyperedge HE-6 (induced by source paper "learning actions from human demonstration video for robotic manipulation"):
  with members:
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "robotic grasp detection using deep convolutional neural networks"
    - "sequence to sequence video to text"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "one shot imitation from observing humans via domain adaptive meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "deep episodic memory encoding recalling and predicting episodic experiences for robot action execution"
    - "virtual to real reinforcement learning for autonomous driving"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "learning to generalize meta learning for domain generalization"
    - "satellite image based localization via learned embeddings"
    - "learning robot activities from first person human videos using convolutional future regression"
    - "third person imitation learning"

Connected through hyperedge HE-8 (induced by source paper "object captioning and retrieval with natural language"):
  with members:
    - "deep reinforcement learning based image captioning with embedding reward"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "top down visual saliency guided by captions"
    - "generation and comprehension of unambiguous object descriptions"
    - "densecap fully convolutional localization networks for dense captioning"

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)