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: "learning pairwise relationship for multi object detection in crowded scenes"
  Abstract: As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years. It is efficient and accurate for sparse scenes, but suffers an inevitable trade-off between precision and recall in crowded scenes. To overcome this drawback, we propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship network that is based on deep learning is learned to predict if two overlapping proposal boxes contain two objects or zero/one object, which can handle multiple overlapping objects effectively. Through neatly coupling with GreedyNMS without losing efficiency, consistent improvements have been achieved in heavily occluded datasets including MOT15, TUD-Crossing and PETS. In addition, Pairwise-NMS can be integrated into any learning based detectors (Both of Faster-RCNN and DPM detectors are tested in this paper), thus building a bridge between GreedyNMS and end-to-end learning detectors.

Connected through hyperedge HE-1 (induced by source paper "learn to predict sets using feed forward neural networks"):
  with members:
    - "rep the set neural networks for learning set representations"
    - "deep set prediction networks"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "yolov3 an incremental improvement"
    - "deep sets"
    - "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "rgbd based dimensional decomposition residual network for 3d semantic scene completion"):
  with members:
    - "efficient semantic scene completion network with spatial group convolution"
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "two stream 3d semantic scene completion"
    - "view volume network for semantic scene completion from a single depth image"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "in defense of osvos"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "mask propagation network for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "yolov3 an incremental improvement"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "fast online object tracking and segmentation a unifying approach"
    - "path aggregation network for instance segmentation"

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)