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: "interactive perception leveraging action in perception and perception in action"
  Abstract: Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

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 "self supervised object in gripper segmentation from robotic motions"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "towards segmenting everything that moves"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "photorealistic image synthesis for object instance detection"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "learning instance segmentation by interaction"
    - "deep visual domain adaptation a survey"
    - "object discovery in videos as foreground motion clustering"

Connected through hyperedge HE-3 (induced by source paper "learning to singulate objects using a push proposal network"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "metric learning for generalizing spatial relations to new objects"
    - "fast model identification via physics engines for data efficient policy search"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "deep visual foresight for planning robot motion"
    - "high precision grasp pose detection in dense clutter"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "the automated inspection of opaque liquid vaccines"):
  with members:
    - "fully convolutional one shot object segmentation for industrial robotics"
    - "fast convergence for object detection by learning how to combine error functions"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "visualizing and understanding atari agents"
    - "densely connected convolutional networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "generating videos with scene dynamics"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "mqa answering the question via robotic manipulation"):
  with members:
    - "improving generative visual dialog by answering diverse questions"
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "neural modular control for embodied question answering"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"

Connected through hyperedge HE-6 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-7 (induced by source paper "self supervised 6d object pose estimation for robot manipulation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning instance segmentation by interaction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "mechanical search multi step retrieval of a target object occluded by clutter"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "one shot segmentation in clutter"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "visual representations for semantic target driven navigation"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "neural task programming learning to generalize across hierarchical tasks"

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)