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: "learning robot activities from first person human videos using convolutional future regression"
  Abstract: We design a new approach that allows robot learning of new activities from unlabeled human example videos. Given videos of humans executing the same activity from a human's viewpoint (i.e., first-person videos), our objective is to make the robot learn the temporal structure of the activity as its future regression network, and learn to transfer such model for its own motor execution. We present a new deep learning model: We extend the state-of-the-art convolutional object detection network for the detection of human hands in training videos based on image information, and newly introduce the concept of using a fully convolutional network to regress (i.e., predict) the intermediate scene representation corresponding to the future frame (e.g., 1-2 seconds later). Combining these allows direct prediction of future locations of human hands and objects, which enables the robot to infer the motor control plan using our manipulation network. We experimentally confirm that our approach makes learning of robot activities from unlabeled human interaction videos possible, and demonstrate that our robot is able to execute the learned collaborative activities in real-time directly based on its camera input.

Connected through hyperedge HE-1 (induced by source paper "analysis of the hands in egocentric vision a survey"):
  with members:
    - "an effective and efficient method for detecting hands in egocentric videos for rehabilitation applications"
    - "recurrent u net for resource constrained segmentation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "domain specific priors and meta learning for low shot first person action recognition"
    - "egocentric hand track and object based human action recognition"
    - "egocentric video a new tool for capturing hand use of individuals with spinal cord injury at home"
    - "scaling egocentric vision the epic kitchens dataset"
    - "analysis of hand segmentation in the wild"

Connected through hyperedge HE-2 (induced by source paper "learning one shot imitation from humans without humans"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "deep episodic memory encoding recalling and predicting episodic experiences for robot action execution"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "task embedded control networks for few shot imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-3 (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"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "deep dual relation modeling for egocentric interaction recognition"):
  with members:
    - "a correlation based feature representation for first person activity recognition"
    - "unsupervised learning of depth and ego motion from video"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "two stream collaborative learning with spatial temporal attention for video classification"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "going deeper into first person activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (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"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "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"
    - "third person imitation learning"

Connected through hyperedge HE-6 (induced by source paper "learning real world robot policies by dreaming"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "world models"
    - "state representation learning for control an overview"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "value prediction network"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-7 (induced by source paper "imitation from observation learning to imitate behaviors from raw video via context translation"):
  with members:
    - "third person imitation learning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "time contrastive networks self supervised learning from video"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "one shot imitation learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "se3 nets learning rigid body motion using deep 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)