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: "introspective perception learning to predict failures in vision systems"
  Abstract: As robots aspire for long-term autonomous operations in complex dynamic environments, the ability to reliably take mission-critical decisions in ambiguous situations becomes critical. This motivates the need to build systems that have situational awareness to assess how qualified they are at that moment to make a decision. We call this self-evaluating capability as introspection. In this paper, we take a small step in this direction and propose a generic framework for introspective behavior in perception systems. Our goal is to learn a model to reliably predict failures in a given system, with respect to a task, directly from input sensor data. We present this in the context of vision-based autonomous MAV flight in outdoor natural environments, and show that it effectively handles uncertain situations.

Connected through hyperedge HE-1 (induced by source paper "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"):
  with members:
    - "estimating risk and uncertainty in deep reinforcement learning"
    - "policies modulating trajectory generators"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "residual policy learning"
    - "on evaluation of embodied navigation agents"
    - "residual reinforcement learning for robot control"
    - "addressing function approximation error in actor critic methods"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "did you miss the sign a false negative alarm system for traffic sign detectors"):
  with members:
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "simultaneous traffic sign detection and boundary estimation using convolutional neural network"
    - "failure prediction for autonomous driving"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "ivoa introspective vision for obstacle avoidance"):
  with members:
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "gonet a semi supervised deep learning approach for traversability estimation"

Connected through hyperedge HE-4 (induced by source paper "learning single image depth from videos using quality assessment networks"):
  with members:
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "soccer on your tabletop"
    - "megadepth learning single view depth prediction from internet photos"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-5 (induced by source paper "failing to learn autonomously identifying perception failures for self driving cars"):
  with members:
    - "deepxplore automated whitebox testing of deep learning systems"
    - "accurate single stage detector using recurrent rolling convolution"
    - "how far are we from solving pedestrian detection"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "ssd single shot multibox detector"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "uncertainty aware reinforcement learning for collision avoidance"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "funnel libraries for real time robust feedback motion planning"
    - "plato policy learning using adaptive trajectory optimization"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"

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)