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: "active object perceiver recognition guided policy learning for object searching on mobile robots"
  Abstract: We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs. While scene-driven visual navigation has been widely studied, prior efforts on learning navigation policies for robots to find objects are limited. The problem is often more challenging than target scene finding as the target objects can be very small in the view and can be in an arbitrary pose. We approach the problem from an active perceiver perspective, and propose a novel framework that integrates a deep neural network based object recognition module and a deep reinforcement learning based action prediction mechanism. To validate our method, we conduct experiments on both a simulation dataset (AI2-THOR) and a real-world environment with a physical robot. We further propose a new decaying reward function to learn the control policy specific to the object searching task. Experimental results validate the efficacy of our method, which outperforms competing methods in both average trajectory length and success rate.

Connected through hyperedge HE-1 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin"):
  with members:
    - "walking with mind mental imagery enhanced embodied qa"
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "graph attention memory for visual navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi target embodied question answering"
    - "learning your way without map or compass panoramic target driven visual navigation"

Connected through hyperedge HE-2 (induced by source paper "embodied visual recognition"):
  with members:
    - "learning instance segmentation by interaction"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gibson env real world perception for embodied agents"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "learning to see the invisible end to end trainable amodal instance segmentation"

Connected through hyperedge HE-3 (induced by source paper "tkd temporal knowledge distillation for active perception"):
  with members:
    - "a novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga"
    - "real world anomaly detection in surveillance videos"
    - "object detection at 200 frames per second"
    - "yolov3 an incremental improvement"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "online model distillation for efficient video inference"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-4 (induced by source paper "simultaneous mapping and target driven navigation"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "learning exploration policies for navigation"
    - "habitat a platform for embodied ai research"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-5 (induced by source paper "active scene understanding via online semantic reconstruction"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "object aware guidance for autonomous scene reconstruction"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "robot in a room toward perfect object recognition in closed environments"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-6 (induced by source paper "egocentric vision based future vehicle localization for intelligent driving assistance systems"):
  with members:
    - "goal oriented object importance estimation in on road driving videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"
    - "joint person segmentation and identification in synchronized first and third person videos"
    - "identifying first person camera wearers in third person videos"

Connected through hyperedge HE-7 (induced by source paper "gaple generalizable approaching policy learning for robotic object searching in indoor environment"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "driving policy transfer via modularity and abstraction"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "visual representations for semantic target driven navigation"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "a dataset for developing and benchmarking active vision"
    - "divide and conquer reinforcement learning"

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.AI(Artificial Intelligence)