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: "deep occlusion reasoning for multi camera multi target detection"
  Abstract: People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Convolutional Neural Nets and Conditional Random Fields to explicitly model those ambiguities. One of its key ingredients are high-order CRF terms that model potential occlusions and give our approach its robustness even when many people are present. Our model is trained end-to-end and we show that it outperforms several state-of-art algorithms on challenging scenes.

Connected through hyperedge HE-1 (induced by source paper "a bayesian filter for multi view 3d multi object tracking with occlusion handling"):
  with members:
    - "deep multi camera people detection"
    - "yolo9000 better faster stronger"
    - "performance measures and a data set for multi target multi camera tracking"
    - "you only look once unified real time object detection"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "fast r cnn"
    - "high speed tracking with kernelized correlation filters"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

Connected through hyperedge HE-2 (induced by source paper "a bayesian 3d multi view multi object tracking filter"):
  with members:
    - "deep multi camera people detection"
    - "yolo9000 better faster stronger"
    - "performance measures and a data set for multi target multi camera tracking"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "fast r cnn"
    - "motchallenge 2015 towards a benchmark for multi target tracking"

Connected through hyperedge HE-3 (induced by source paper "self supervised training of proposal based segmentation via background prediction"):
  with members:
    - "unsupervised learning of foreground object segmentation"
    - "neural scene decomposition for multi person motion capture"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "generative image inpainting with contextual attention"
    - "unsupervised object discovery and co localization by deep descriptor transforming"
    - "the 2017 davis challenge on video object segmentation"
    - "learning video object segmentation with visual memory"
    - "segflow joint learning for video object segmentation and optical flow"

Connected through hyperedge HE-4 (induced by source paper "lidar based object classification with explicit occlusion modeling"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "tracking by prediction a deep generative model for mutli person localisation and tracking"):
  with members:
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "deep multi camera people detection"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "semantic driven multi camera pedestrian detection"):
  with members:
    - "understanding convolution for semantic segmentation"
    - "deep multi camera people detection"
    - "pyramid scene parsing network"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "oatm occlusion aware template matching by consensus set maximization"):
  with members:
    - "unsupervised deep homography a fast and robust homography estimation model"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "deep image homography estimation"
    - "template matching with deformable diversity similarity"

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)