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 to augment synthetic images for sim2real policy transfer"
  Abstract: Vision and learning have made significant progress that could improve robotics policies for complex tasks and environments. Learning deep neural networks for image understanding, however, requires large amounts of domain-specific visual data. While collecting such data from real robots is possible, such an approach limits the scalability as learning policies typically requires thousands of trials. In this work we attempt to learn manipulation policies in simulated environments. Simulators enable scalability and provide access to the underlying world state during training. Policies learned in simulators, however, do not transfer well to real scenes given the domain gap between real and synthetic data. We follow recent work on domain randomization and augment synthetic images with sequences of random transformations. Our main contribution is to optimize the augmentation strategy for sim2real transfer and to enable domain-independent policy learning. We design an efficient search for depth image augmentations using object localization as a proxy task. Given the resulting sequence of random transformations, we use it to augment synthetic depth images during policy learning. Our augmentation strategy is policy-independent and enables policy learning with no real images. We demonstrate our approach to significantly improve accuracy on three manipulation tasks evaluated on a real robot.

Connected through hyperedge HE-1 (induced by source paper "learning to combine primitive skills a step towards versatile robotic manipulation"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "neural modular control for embodied question answering"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-2 (induced by source paper "manga method agnostic neural policy generalization and adaptation"):
  with members:
    - "sim to real transfer for biped locomotion"
    - "norml no reward meta learning"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "exploration by random network distillation"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"

Connected through hyperedge HE-3 (induced by source paper "combining learned skills and reinforcement learning for robotic manipulations"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "latent space policies for hierarchical reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "fast policy learning through imitation and reinforcement"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "asymmetric actor critic for image based robot learning"

Connected through hyperedge HE-4 (induced by source paper "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"):
  with members:
    - "monocular depth estimation a survey"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"

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)