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: "navigation by imitation in a pedestrian rich environment"
  Abstract: Deep neural networks trained on demonstrations of human actions give robot the ability to perform self-driving on the road. However, navigation in a pedestrian-rich environment, such as a campus setup, is still challenging---one needs to take frequent interventions to the robot and take control over the robot from early steps leading to a mistake. An arduous burden is, hence, placed on the learning framework design and data acquisition. In this paper, we propose a new learning-from-intervention Dataset Aggregation (DAgger) algorithm to overcome the limitations brought by applying imitation learning to navigation in the pedestrian-rich environment. Our new learning algorithm implements an error backtrack function that is able to effectively learn from expert interventions. Combining our new learning algorithm with deep convolutional neural networks and a hierarchically-nested policy-selection mechanism, we show that our robot is able to map pixels direct to control commands and navigate successfully in real world without explicitly modeling the pedestrian behaviors or the world model.

Connected through hyperedge HE-1 (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"
    - "driving policy transfer via modularity and abstraction"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "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-2 (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"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "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-3 (induced by source paper "learning from interventions using hierarchical policies for safe learning"):
  with members:
    - "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "cycle of learning for autonomous systems from human interaction"
    - "hierarchical imitation and reinforcement learning"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "hindsight experience replay"
    - "carla an open urban driving simulator"
    - "curiosity driven exploration by self supervised prediction"

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.CV(Computer Vision and Pattern Recognition)