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: "gplac generalizing vision based robotic skills using weakly labeled images"
  Abstract: We tackle the problem of learning robotic sensorimotor control policies that can generalize to visually diverse and unseen environments. Achieving broad generalization typically requires large datasets, which are difficult to obtain for task-specific interactive processes such as reinforcement learning or learning from demonstration. However, much of the visual diversity in the world can be captured through passively collected datasets of images or videos. In our method, which we refer to as GPLAC (Generalized Policy Learning with Attentional Classifier), we use both interaction data and weakly labeled image data to augment the generalization capacity of sensorimotor policies. Our method combines multitask learning on action selection and an auxiliary binary classification objective, together with a convolutional neural network architecture that uses an attentional mechanism to avoid distractors. We show that pairing interaction data from just a single environment with a diverse dataset of weakly labeled data results in greatly improved generalization to unseen environments, and show that this generalization depends on both the auxiliary objective and the attentional architecture that we propose. We demonstrate our results in both simulation and on a real robotic manipulator, and demonstrate substantial improvement over standard convolutional architectures and domain adaptation methods.

Connected through hyperedge HE-1 (induced by source paper "online meta learning"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "growing a brain fine tuning by increasing model capacity"
    - "introduction to online convex optimization"
    - "provable guarantees for gradient based meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "truncated back propagation for bilevel optimization"
    - "how to train your maml"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-2 (induced by source paper "self supervised correspondence in visuomotor policy learning"):
  with members:
    - "unsupervised learning of object keypoints for perception and control"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "graph structured visual imitation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised visuomotor control through distributional planning networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-3 (induced by source paper "adversarial feature training for generalizable robotic visuomotor control"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "affordance learning for end to end visuomotor robot control"
    - "beyond sharing weights for deep domain adaptation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep reinforcement learning to acquire navigation skills for wheel legged robots in complex environments"
    - "deep object centric representations for generalizable robot learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "affordance learning for end to end visuomotor robot control"):
  with members:
    - "visual affordance and function understanding a survey"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "deep object centric representations for generalizable robot learning"
    - "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"
    - "learning to act properly predicting and explaining affordances from images"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "deep predictive policy training using reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "w talc weakly supervised temporal activity localization and classification"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "transfer learning by ranking for weakly supervised object annotation"

Connected through hyperedge HE-6 (induced by source paper "constraint estimation and derivative free recovery for robot learning from demonstrations"):
  with members:
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "dart noise injection for robust imitation learning"
    - "constrained policy optimization"
    - "sprk a low cost stewart platform for motion study in surgical robotics"

Connected through hyperedge HE-7 (induced by source paper "sim to real transfer of accurate grasping with eye in hand observations and continuous control"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "transfer learning from synthetic to real images using variational autoencoders for robotic applications"
    - "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"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "sim to real robot learning from pixels with progressive nets"

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.LG(Machine Learning)