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: "a survey of object classification and detection based on 2d 3d data"
  Abstract: Recently, by using deep neural network based algorithms, object classification, detection and semantic segmentation solutions are significantly improved. However, one challenge for 2D image-based systems is that they cannot provide accurate 3D location information. This is critical for location sensitive applications such as autonomous driving and robot navigation. On the other hand, 3D methods, such as RGB-D and RGB-LiDAR based systems, can provide solutions that significantly improve the RGB only approaches. That is why this is an interesting research area for both industry and academia. Compared with 2D image-based systems, 3D-based systems are more complicated due to the following five reasons: 1) Data representation itself is more complicated. 3D images can be represented by point clouds, meshes, volumes. 2D images have pixel grid representations. 2) The computation and memory resource requirement is higher as an extra dimension is added. 3) Different distribution of the objects and difference in scene areas between indoor and outdoor make one unified framework hard to achieve. 4) 3D data, especially for the outdoor scenario, is sparse compared with the dense 2D images which makes the detection task more challenging. Finally, large size labelled datasets, which are extremely important for supervised based algorithms, are still under construction compared with well-built 2D datasets such as ImageNet. Based on challenges listed above, the described systems are organized by application scenarios, data representation methods and main tasks addressed. At the same time, critical 2D based systems which greatly influence the 3D ones are also introduced to show the connection between them.

Connected through hyperedge HE-1 (induced by source paper "frustum voxnet for 3d object detection from rgb d or depth images"):
  with members:
    - "deep hough voting for 3d object detection in point clouds"
    - "group normalization"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mask r cnn"
    - "yolo9000 better faster stronger"

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)