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: "cooperative starting movement detection of cyclists using convolutional neural networks and a boosted stacking ensemble"
  Abstract: In the future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this paper, we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing feature- and decision-level cooperation. We introduce a novel method based on a three-dimensional convolutional neural network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.

Connected through hyperedge HE-1 (induced by source paper "smart device based initial movement detection of cyclists using convolutional neuronal networks"):
  with members:
    - "early start intention detection of cyclists using motion history images and a deep residual network"
    - "detecting intentions of vulnerable road users based on collective intelligence"
    - "transforming sensor data to the image domain for deep learning an application to footstep detection"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "driver behavior recognition via interwoven deep convolutional neural nets with multi stream inputs"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning in mobile and wireless networking a survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "starting movement detection of cyclists using smart devices"):
  with members:
    - "early start intention detection of cyclists using motion history images and a deep residual network"
    - "feature selection a data perspective"
    - "xgboost a scalable tree boosting system"

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)