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: "recognizing objects in the wild where do we stand"
  Abstract: The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, preventing the use of autonomous agents for real-world applications. The progress is slowed down by the lack of a testbed able to accurately represent the world perceived by the robot in-the-wild. In order to fill this gap, we introduce a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. The dataset embeds the challenges faced by a robot in a real-life application and provides a useful tool for validating object recognition algorithms. Besides describing the characteristics of the dataset, the paper evaluates the performance of a collection of well-established deep convolutional networks on the new dataset and analyzes the transferability of deep representations from Web images to robotic data. Despite the promising results obtained with such representations, the experiments demonstrate that object classification with real-life robotic data is far from being solved. Finally, we provide a comparative study to analyze and highlight the open challenges in robot vision, explaining the discrepancies in the performance.

Connected through hyperedge HE-1 (induced by source paper "openloris object a robotic vision dataset and benchmark for lifelong deep learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "are we ready for service robots the openloris scene datasets for lifelong slam"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "incremental classifier learning with generative adversarial networks"
    - "the limits and potentials of deep learning for robotics"
    - "don t forget there is more than forgetting new metrics for continual learning"

Connected through hyperedge HE-2 (induced by source paper "verefine integrating object pose verification with iterative physics guided refinement"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "graspa 1 0 graspa is a robot arm grasping performance benchmark"
    - "dpod 6d pose object detector and refiner"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"

Connected through hyperedge HE-3 (induced by source paper "customizing object detectors for indoor robots"):
  with members:
    - "yolov3 an incremental improvement"
    - "intelligently assisting human guided quadcopter photography"
    - "dsod learning deeply supervised object detectors from scratch"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "beyond skip connections top down modulation for object detection"

Connected through hyperedge HE-4 (induced by source paper "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"):
  with members:
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "rgbd datasets past present and future"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "semanticpaint interactive 3d labeling and learning at your fingertips"

Connected through hyperedge HE-5 (induced by source paper "visual curiosity learning to ask questions to learn visual recognition"):
  with members:
    - "neural baby talk"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning algorithms for active learning"
    - "learning how to learn an adaptive dialogue agent for incrementally learning visually grounded word meanings"
    - "iqa visual question answering in interactive environments"
    - "a meta learning approach to one step active learning"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "embodied question answering"

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)