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: "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
  Abstract: Deep reinforcement learning (DRL) demonstrates its potential in learning a model-free navigation policy for robot visual navigation. However, the data-demanding algorithm relies on a large number of navigation trajectories in training. Existing datasets supporting training such robot navigation algorithms consist of either 3D synthetic scenes or reconstructed scenes. Synthetic data suffers from domain gap to the real-world scenes while visual inputs rendered from 3D reconstructed scenes have undesired holes and artifacts. In this paper, we present a new dataset collected in real-world to facilitate the research in DRL based visual navigation. Our dataset includes 3D reconstruction for real-world scenes as well as densely captured real 2D images from the scenes. It provides high-quality visual inputs with real-world scene complexity to the robot at dense grid locations. We further study and benchmark one recent DRL based navigation algorithm and present our attempts and thoughts on improving its generalizability to unseen test targets in the scenes.

Connected through hyperedge HE-1 (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-2 (induced by source paper "cross view policy learning for street navigation"):
  with members:
    - "learning to follow directions in street view"
    - "the streetlearn environment and dataset"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "global pose estimation with an attention based recurrent network"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "unsupervised predictive memory in a goal directed agent"
    - "modality distillation with multiple stream networks for action recognition"

Connected through hyperedge HE-3 (induced by source paper "the streetlearn environment and dataset"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "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"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-4 (induced by source paper "lerop a learning based modular robot photography framework"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "control of a quadrotor with reinforcement learning"
    - "convolutional pose machines"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "yolo9000 better faster stronger"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "omnidepth dense depth estimation for indoors spherical panoramas"):
  with members:
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"
    - "light weight pixel context encoders for image inpainting"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "learning high dynamic range from outdoor panoramas"
    - "active convolution learning the shape of convolution for image classification"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "learning to navigate in cities without a map"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "semi parametric topological memory for navigation"
    - "a critical investigation of deep reinforcement learning for navigation"
    - "understanding grounded language learning agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active neural localization"
    - "unsupervised predictive memory in a goal directed agent"
    - "automated curriculum learning for neural 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)