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: "camera placement based on vehicle traffic for better city security surveillance"
  Abstract: Security surveillance is important in smart cities. Deploying numerous cameras is a common approach. Given the importance of vehicles in a metropolis, using vehicle traffic patterns to strategically place cameras could potentially facilitate security surveillance. This article constitutes the first effort toward building the link between vehicle traffic and camera placement for better security surveillance.

Connected through hyperedge HE-1 (induced by source paper "green security game with community engagement"):
  with members:
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-2 (induced by source paper "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"):
  with members:
    - "mobinet a mobile binary network for image classification"
    - "pack and detect fast object detection in videos using region of interest packing"
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-3 (induced by source paper "prediction tracking segmentation"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "fast online object tracking and segmentation a unifying approach"
    - "towards a better match in siamese network based visual object tracker"

Connected through hyperedge HE-4 (induced by source paper "feature selective anchor free module for single shot object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bounding box regression with uncertainty for accurate object detection"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "seeing small faces from robust anchor s perspective"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "detnet a backbone network for object detection"
    - "ring loss convex feature normalization for face recognition"
    - "sface an efficient network for face detection in large scale variations"

Connected through hyperedge HE-5 (induced by source paper "deep multivariate mixture of gaussians for object detection under occlusion"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "deformable convnets v2 more deformable better results"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-6 (induced by source paper "bounding box regression with uncertainty for accurate object detection"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "cornernet detecting objects as paired keypoints"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "acquisition of localization confidence for accurate object detection"

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.CY(Computers and Society)