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: "a data efficient framework for training and sim to real transfer of navigation policies"
  Abstract: Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration. In the case of a robot operating in a real environment the training process can be costly, time-consuming, and even dangerous since failures are common at the start of training. For this reason, it is desirable to be able to leverage \textit{simulation} and \textit{off-policy} data to the extent possible to train the robot. In this work, we introduce a robust framework that plans in simulation and transfers well to the real environment. Our model incorporates a gradient-descent based planning module, which, given the initial image and goal image, encodes the images to a lower dimensional latent state and plans a trajectory to reach the goal. The model, consisting of the encoder and planner modules, is trained through a meta-learning strategy in simulation first. We subsequently perform adversarial domain transfer on the encoder by using a bank of unlabelled but random images from the simulation and real environments to enable the encoder to map images from the real and simulated environments to a similarly distributed latent representation. By fine tuning the entire model (encoder + planner) with far fewer real world expert demonstrations, we show successful planning performances in different navigation tasks.

Connected through hyperedge HE-1 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin"):
  with members:
    - "walking with mind mental imagery enhanced embodied qa"
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "graph attention memory for visual navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi target embodied question answering"
    - "learning your way without map or compass panoramic target driven visual navigation"

Connected through hyperedge HE-2 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "f1 10 an open source autonomous cyber physical platform"
    - "mushr a low cost open source robotic racecar for education and research"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "self driving scale car trained by deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"

Connected through hyperedge HE-3 (induced by source paper "manga method agnostic neural policy generalization and adaptation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning to navigate from simulation via spatial and semantic information synthesis"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "virtual to real learning to control in visual semantic segmentation"
    - "navigation by imitation in a pedestrian rich environment"
    - "driving policy transfer via modularity and abstraction"
    - "visual representations for semantic target driven navigation"
    - "virtual to real reinforcement learning for autonomous driving"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"

Connected through hyperedge HE-5 (induced by source paper "learning to navigate from simulation via spatial and semantic information synthesis with noise model embedding"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "navigation by imitation in a pedestrian rich environment"
    - "virtual to real reinforcement learning for autonomous driving"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "unsupervised learning of depth and ego motion from video"
    - "learning to fly by crashing"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a generative framework for zero shot learning with adversarial domain adaptation"):
  with members:
    - "transductive unbiased embedding for zero shot learning"
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "cycada cycle consistent adversarial domain adaptation"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "automated shapeshifting for function recovery in damaged robots"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "interoceptive robustness through environment mediated morphological development"
    - "a minimal developmental model can increase evolvability in soft robots"
    - "robots that can adapt like animals"
    - "multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation"

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)