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: "a study on wrist identification for forensic investigation"
  Abstract: Abstract   Criminal and victim identification based on crime scene images is an important part of forensic investigation. Criminals usually avoid identification by covering their faces and tattoos in the evidence images, which are taken in uncontrolled environments. Existing identification methods, which make use of biometric traits, such as vein, skin mark, height, skin colour, weight, race, etc., are considered for solving this problem. The soft biometric traits, including skin colour, gender, height, weight and race, provide useful information but not distinctive enough. Veins and skin marks are limited to high resolution images and some body sites may neither have enough skin marks nor clear veins. Terrorists and rioters tend to expose their wrists in a gesture of triumph, greeting or salute, while paedophiles usually show them when touching victims. However, wrists were neglected by the biometric community for forensic applications. In this paper, a wrist identification algorithm, which includes skin segmentation, key point localisation , image to template alignment, large feature set extraction, and classification, is proposed. The proposed algorithm is evaluated on NTU-Wrist-Image-Database-v1, which consists of 3945 images from 731 different wrists, including 205 pairs of wrist images collected from the Internet, taken under uneven illuminations with different poses and resolutions. The experimental results show that wrist is a useful clue for criminal and victim identification.

Connected through hyperedge HE-1 (induced by source paper "towards unconstrained palmprint recognition on consumer devices a literature review"):
  with members:
    - "palmprint recognition in uncontrolled and uncooperative environment"
    - "pilot comparative study of different deep features for palmprint identification in low quality images"
    - "11k hands gender recognition and biometric identification using a large dataset of hand images"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "convolutional pose machines"
    - "ternary weight 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)