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: "detecting zones and threat on 3d body for security in airports using deep machine learning"
  Abstract: In this research, it was used a segmentation and classification method to identify threat recognition in human scanner images of airport security. The Department of Homeland Security's (DHS) in USA has a higher false alarm, produced from theirs algorithms using today's scanners at the airports. To repair this problem they started a new competition at Kaggle site asking the science community to improve their detection with new algorithms. The dataset used in this research comes from DHS at this https URL According to DHS: "This dataset contains a large number of body scans acquired by a new generation of millimeter wave scanner called the High Definition-Advanced Imaging Technology (HD-AIT) system. They are comprised of volunteers wearing different clothing types (from light summer clothes to heavy winter clothes), different body mass indices, different genders, different numbers of threats, and different types of threats". Using Python as a principal language, the preprocessed of the dataset images extracted features from 200 bodies using: intensity, intensity differences and local neighbourhood to detect, to produce segmentation regions and label those regions to be used as a truth in a training and test dataset. The regions are subsequently give to a CNN deep learning classifier to predict 17 classes (that represents the body zones): zone1, zone2, ... zone17 and zones with threat in a total of 34 zones. The analysis showed the results of the classifier an accuracy of 98.2863% and a loss of 0.091319, as well as an average of 100% for recall and precision.

Connected through hyperedge HE-1 (induced by source paper "identifying and correcting label bias in machine learning"):
  with members:
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "detecting zones and threat on 3d body in security airports using deep learning machine"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

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)