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: "revisiting active perception"
  Abstract: Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.

Connected through hyperedge HE-1 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "model based reinforcement learning for atari"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "end to end pixel based deep active inference for body perception and action"):
  with members:
    - "deep active inference as variational policy gradients"
    - "sensorimotor learning for artificial body perception"
    - "active inference body perception and action for humanoid robots"
    - "scaling active inference"
    - "multimodal representation models for prediction and control from partial information"
    - "visual reinforcement learning with imagined goals"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "active perception with a monocular camera for multiscopic vision"):
  with members:
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "reinforcement learning in topology based representation for human body movement with whole arm manipulation"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-4 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"

Connected through hyperedge HE-5 (induced by source paper "video object segmentation based visual servo control and object depth estimation on a mobile robot platform"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "self supervised robot in hand object learning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-6 (induced by source paper "kinematically informed interactive perception robot generated 3d models for classification"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "mask r cnn"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "high precision grasp pose detection in dense clutter"
    - "ssd single shot multibox detector"
    - "r fcn object detection via region based fully convolutional networks"
    - "fast r cnn"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-7 (induced by source paper "higher order function networks for view planning and multi view reconstruction"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "pixels to plans learning non prehensile manipulation by imitating a planner"
    - "higher order function networks for learning composable 3d object representations"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "mask r cnn"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-8 (induced by source paper "domain randomization for active pose estimation"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization and generative models for robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "cad2rl real single image flight without a single real image"
    - "learning contact rich manipulation skills with guided policy search"
    - "very deep convolutional networks for large scale image recognition"

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)