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: "11k hands gender recognition and biometric identification using a large dataset of hand images"
  Abstract: The human hand possesses distinctive features which can reveal gender information. In addition, the hand is considered one of the primary biometric traits used to identify a person. In this work, we propose a large dataset of human hand images (dorsal and palmar sides) with detailed ground-truth information for gender recognition and biometric identification. Using this dataset, a convolutional neural network (CNN) can be trained effectively for the gender recognition task. Based on this, we design a two-stream CNN to tackle the gender recognition problem. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for the biometric identification task. We show that the dorsal side of hand images, captured by a regular digital camera, convey effective distinctive features similar to, if not better, those available in the palmar hand images. To facilitate access to the proposed dataset and replication of our experiments, the dataset, trained CNN models, and Matlab source code are available at (this https URL).

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"
    - "a study on wrist identification for forensic investigation"
    - "pilot comparative study of different deep features for palmprint identification in low quality 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"

Connected through hyperedge HE-2 (induced by source paper "finding solutions to generative adversarial privacy"):
  with members:
    - "minimax filter learning to preserve privacy from inference attacks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "child gender determination with convolutional neural networks on hand radio graphs"):
  with members:
    - "learning deep features for discriminative localization"

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)