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: "mid level visual representations improve generalization and sample efficiency for learning active tasks"
  Abstract: One of the ultimate promises of computer vision is to help robotic agents perform active tasks, like delivering packages or doing household chores. However, the conventional approach to solving "vision" is to define a set of offline recognition problems (e.g. object detection) and solve those first. This approach faces a challenge from the recent rise of Deep Reinforcement Learning frameworks that learn active tasks from scratch using images as input. This poses a set of fundamental questions: what is the role of computer vision if everything can be learned from scratch? Could intermediate vision tasks actually be useful for performing arbitrary downstream active tasks? #R##N#We show that proper use of mid-level perception confers significant advantages over training from scratch. We implement a perception module as a set of mid-level visual representations and demonstrate that learning active tasks with mid-level features is significantly more sample-efficient than scratch and able to generalize in situations where the from-scratch approach fails. However, we show that realizing these gains requires careful selection of the particular mid-level features for each downstream task. Finally, we put forth a simple and efficient perception module based on the results of our study, which can be adopted as a rather generic perception module for active frameworks.

Connected through hyperedge HE-1 (induced by source paper "scaling and benchmarking self supervised visual representation learning"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "gibson env real world perception for embodied agents"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "learning to separate object sounds by watching unlabeled video"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-2 (induced by source paper "visuomotor understanding for representation learning of driving scenes"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geometry aware deep network for single image novel view synthesis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "stereo magnification learning view synthesis using multiplane images"
    - "deep view morphing"
    - "transformation grounded image generation network for novel 3d view synthesis"

Connected through hyperedge HE-3 (induced by source paper "splitnet sim2sim and task2task transfer for embodied visual navigation"):
  with members:
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "habitat a platform for embodied ai research"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "taskonomy disentangling task transfer learning"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"
    - "on evaluation of embodied navigation agents"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-4 (induced by source paper "shapemask learning to segment novel objects by refining shape priors"):
  with members:
    - "towards segmenting everything that moves"
    - "weakly supervised instance segmentation using class peak response"
    - "affinity derivation and graph merge for instance segmentation"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "semi convolutional operators for instance segmentation"
    - "focal loss for dense object detection"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-5 (induced by source paper "habitat a platform for embodied ai research"):
  with members:
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "learning agile and dynamic motor skills for legged robots"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "virtualhome simulating household activities via programs"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-6 (induced by source paper "where are the keys learning object centric navigation policies on semantic maps with graph convolutional networks"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "graph policy gradients for large scale robot control"
    - "representation learning on graphs a reinforcement learning application"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "deep slam object level rgbd slam based on class specific deep shape priors"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"

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)