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: "gated path planning networks"
  Abstract: Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random seed sensitivity, and other optimization problems. In this work, we reframe VINs as recurrent-convolutional networks which demonstrates that VINs couple recurrent convolutions with an unconventional max-pooling activation. From this perspective, we argue that standard gated recurrent update equations could potentially alleviate the optimization issues plaguing VIN. The resulting architecture, which we call the Gated Path Planning Network, is shown to empirically outperform VIN on a variety of metrics such as learning speed, hyperparameter sensitivity, iteration count, and even generalization. Furthermore, we show that this performance gap is consistent across different maze transition types, maze sizes and even show success on a challenging 3D environment, where the planner is only provided with first-person RGB images.

Connected through hyperedge HE-1 (induced by source paper "deep compositional robotic planners that follow natural language commands"):
  with members:
    - "from semantics to execution integrating action planning with reinforcement learning for robotic causal problem solving"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "from semantics to execution integrating action planning with reinforcement learning for robotic tool use"
    - "prospection interpretable plans from language by predicting the future"
    - "learning to plan via neural exploration exploitation trees"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "deep sequential models for sampling based planning"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "search on the replay buffer bridging planning and reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "learning latent plans from play"
    - "the laplacian in rl learning representations with efficient approximations"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "learning navigation behaviors end to end with autorl"

Connected through hyperedge HE-3 (induced by source paper "neonav improving the generalization of visual navigation via generating next expected observations"):
  with members:
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "habitat a platform for embodied ai research"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning and querying fast generative models for reinforcement learning"
    - "visual semantic navigation using scene priors"
    - "learning to navigate in cities without a map"

Connected through hyperedge HE-4 (induced by source paper "benchmarking classic and learned navigation in complex 3d environments"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "group normalization"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-5 (induced by source paper "an investigation of model free planning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational deep reinforcement learning"
    - "learning to search with mctsnets"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-6 (induced by source paper "learning to plan via neural exploration exploitation trees"):
  with members:
    - "learning to search with mctsnets"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep sequential models for sampling based planning"
    - "learning implicit sampling distributions for motion planning"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning sampling distributions for robot motion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-7 (induced by source paper "value iteration networks on multiple levels of abstraction"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning generalized reactive policies using deep neural networks"
    - "generalized value iteration networks life beyond lattices"
    - "anytime hybrid driving stepping locomotion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-8 (induced by source paper "gan path finder preliminary results"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "value iteration networks on multiple levels of abstraction"
    - "learning to navigate in cities without a map"
    - "improved training of wasserstein gans"
    - "cognitive mapping and planning for visual navigation"
    - "deep visual foresight for planning robot motion"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "image to image translation with conditional 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.LG(Machine Learning)