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 multi camera people detection"
  Abstract: This paper addresses the problem of multi-view people occupancy map estimation. Existing solutions for this problem either operate per-view, or rely on a background subtraction pre-processing. Both approaches lessen the detection performance as scenes become more crowded. The former does not exploit joint information, whereas the latter deals with ambiguous input due to the foreground blobs becoming more and more interconnected as the number of targets increases. #R##N#Although deep learning algorithms have proven to excel on remarkably numerous computer vision tasks, such a method has not been applied yet to this problem. In large part this is due to the lack of large-scale multi-camera data-set. #R##N#The core of our method is an architecture which makes use of monocular pedestrian data-set, available at larger scale then the multi-view ones, applies parallel processing to the multiple video streams, and jointly utilises it. Our end-to-end deep learning method outperforms existing methods by large margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly available a new three-camera HD data-set. Our source code and trained models will be made available under an open-source license.

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 occlusion reasoning for multi camera multi target 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 occlusion reasoning for multi camera multi target 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 "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"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "deep occlusion reasoning for multi camera multi target detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-4 (induced by source paper "semantic driven multi camera pedestrian detection"):
  with members:
    - "understanding convolution for semantic segmentation"
    - "deep occlusion reasoning for multi camera multi target 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-5 (induced by source paper "deep occlusion reasoning for multi camera multi target detection"):
  with members:
    - "the wildtrack multi camera person dataset"
    - "how far are we from solving pedestrian detection"
    - "ssd single shot multibox detector"
    - "multi modal mean fields via cardinality based clamping"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "principled parallel mean field inference for discrete random fields"
    - "higher order potentials in end to end trainable conditional random fields"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "the wildtrack multi camera person dataset"):
  with members:
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "faster r cnn towards real time object detection with region proposal networks"

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)