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: "does computer vision matter for action"
  Abstract: Computer vision produces representations of scene content. Much computer vision research is predicated on the assumption that these intermediate representations are useful for action. Recent work at the intersection of machine learning and robotics calls this assumption into question by training sensorimotor systems directly for the task at hand, from pixels to actions, with no explicit intermediate representations. Thus the central question of our work: Does computer vision matter for action? We probe this question and its offshoots via immersive simulation, which allows us to conduct controlled reproducible experiments at scale. We instrument immersive three-dimensional environments to simulate challenges such as urban driving, off-road trail traversal, and battle. Our main finding is that computer vision does matter. Models equipped with intermediate representations train faster, achieve higher task performance, and generalize better to previously unseen environments. A video that summarizes the work and illustrates the results can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "self supervised deep pose corrections for robust visual odometry"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "geometric consistency for self supervised end to end visual odometry"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "a micro lie theory for state estimation in robotics"
    - "dpc net deep pose correction for visual localization"

Connected through hyperedge HE-2 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning human optical flow"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-3 (induced by source paper "generating 3d people in scenes without people"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning elementary structures for 3d shape generation and matching"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "the replica dataset a digital replica of indoor spaces"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"
    - "learning to sit synthesizing human chair interactions via hierarchical control"

Connected through hyperedge HE-4 (induced by source paper "learning to navigate using mid level visual priors"):
  with members:
    - "habitat a platform for embodied ai research"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "visual memory for robust path following"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-5 (induced by source paper "learning by cheating"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "monocular plan view networks for autonomous driving"
    - "objects as points"
    - "pytorch an imperative style high performance deep learning library"
    - "an algorithmic perspective on imitation learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

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)