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: "follownet robot navigation by following natural language directions with deep reinforcement learning"
  Abstract: Understanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. We present FollowNet, an end-to-end differentiable neural architecture for learning multi-modal navigation policies. FollowNet maps natural language instructions as well as visual and depth inputs to locomotion primitives. FollowNet processes instructions using an attention mechanism conditioned on its visual and depth input to focus on the relevant parts of the command while performing the navigation task. Deep reinforcement learning (RL) a sparse reward learns simultaneously the state representation, the attention function, and control policies. We evaluate our agent on a dataset of complex natural language directions that guide the agent through a rich and realistic dataset of simulated homes. We show that the FollowNet agent learns to execute previously unseen instructions described with a similar vocabulary, and successfully navigates along paths not encountered during training. The agent shows 30% improvement over a baseline model without the attention mechanism, with 52% success rate at novel instructions.

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"
    - "gated path planning networks"

Connected through hyperedge HE-2 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learning to map natural language instructions to physical quadcopter control using simulated flight"):
  with members:
    - "stay on the path instruction fidelity in vision and language navigation"
    - "executing instructions in situated collaborative interactions"
    - "chasing ghosts instruction following as bayesian state tracking"
    - "universal planning networks"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "speaker follower models for vision and language navigation"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-5 (induced by source paper "conditional driving from natural language instructions"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "prospection interpretable plans from language by predicting the future"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "driving policy transfer via modularity and abstraction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "stay on the path instruction fidelity in vision and language navigation"):
  with members:
    - "multi modal discriminative model for vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "chalet cornell house agent learning environment"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-7 (induced by source paper "enabling robots to understand incomplete natural language instructions using commonsense reasoning"):
  with members:
    - "improving grounded natural language understanding through human robot dialog"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "a simple method for commonsense reasoning"
    - "attention based natural language grounding by navigating virtual environment"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"

Connected through hyperedge HE-8 (induced by source paper "evolving rewards to automate reinforcement learning"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning navigation behaviors end to end with autorl"
    - "evolution guided policy gradient in reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "learning to navigate the web"
    - "learning to walk via deep reinforcement learning"

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)