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: "sim to real robot learning from pixels with progressive nets"
  Abstract: Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in simulated environments. We propose using progressive networks to bridge the reality gap and transfer learned policies from simulation to the real world. The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills. We present an early demonstration of this approach with a number of experiments in the domain of robot manipulation that focus on bridging the reality gap. Unlike other proposed approaches, our real-world experiments demonstrate successful task learning from raw visual input on a fully actuated robot manipulator. Moreover, rather than relying on model-based trajectory optimisation, the task learning is accomplished using only deep reinforcement learning and sparse rewards.

Connected through hyperedge HE-1 (induced by source paper "deep drone racing from simulation to reality with domain randomization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep drone racing learning agile flight in dynamic environments"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "autonomous drone race a computationally efficient vision based navigation and control strategy"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"

Connected through hyperedge HE-2 (induced by source paper "attention privileged reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "state representation learning for control an overview"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "on the continuity of rotation representations in neural networks"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-3 (induced by source paper "zero shot reinforcement learning with deep attention convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reinforcement learning with attention that works a self supervised approach"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "vmav c a deep attention based reinforcement learning algorithm for model based control"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"

Connected through hyperedge HE-4 (induced by source paper "assembling semantically disentangled representations for predictive generative models via adaptation from synthetic domain"):
  with members:
    - "image to image translation for cross domain disentanglement"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning from simulated and unsupervised images through adversarial training"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "understanding neural networks through deep visualization"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "assembly robots with optimized control stiffness through reinforcement learning"):
  with members:
    - "fast skill learning for variable compliance robotic assembly"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "progressive neural architecture search"
    - "deep reinforcement learning for high precision assembly tasks"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-6 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-7 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-8 (induced by source paper "show attend and translate unsupervised image translation with self regularization and attention"):
  with members:
    - "few shot unsupervised image to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "attngan fine grained text to image generation with attentional generative adversarial 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)