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: "faster bounding box annotation for object detection in indoor scenes"
  Abstract: This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. We experimentally study which first/second stage split minimizes to total workload. In addition, we introduce a new fully labeled object detection dataset collected from indoor scenes. Compared to other indoor datasets, our collection has more class categories, diverse backgrounds, lighting conditions, occlusions and high intra-class differences. We train deep learning based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy. The fully annotated dataset is released freely available for the research community.

Connected through hyperedge HE-1 (induced by source paper "act perceive and plan in belief space for robot localization"):
  with members:
    - "semantic visual localization"
    - "towards blended reactive planning and acting using behavior trees"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "distant vehicle detection using radar and vision"):
  with members:
    - "deep learning is robust to massive label noise"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "soft sampling for robust object detection"
    - "detecting traffic lights by single shot detection"
    - "focal loss for dense object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "dsod learning deeply supervised object detectors from scratch"

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)