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: "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
  Abstract: Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition. Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial features and a holistic feature which we call the foggy face. Then, we use these features to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class. We evaluate our method on four challenging datasets to demonstrate its efficacy in achieving better or on-par accuracy with state-of-the-art methods. In addition, we present a new face dataset that intensifies the challenges of occluded faces and illumination changes, which we believe to be a much-needed resource for gender classification research.

Connected through hyperedge HE-1 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns"):
  with members:
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "smoothgrad removing noise by adding noise"
    - "an overview of multi task learning in deep neural networks"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "cross stitch networks for multi task learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "densely connected convolutional networks"
    - "some like it hot visual guidance for preference prediction"

Connected through hyperedge HE-2 (induced by source paper "robust subspace clustering with compressed data"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "automatic subspace learning via principal coefficients embedding"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"

Connected through hyperedge HE-3 (induced by source paper "speed up and multi view extensions to subclass discriminant analysis"):
  with members:
    - "generalized multi view embedding for visual recognition and cross modal retrieval"

Connected through hyperedge HE-4 (induced by source paper "the price of fair pca one extra dimension"):
  with members:
    - "convex formulations for fair principal component analysis"
    - "datasheets for datasets"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "fairness incentives for myopic agents"
    - "runaway feedback loops in predictive policing"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-5 (induced by source paper "11k hands gender recognition and biometric identification using a large dataset of hand images"):
  with members:
    - "which facial expressions can reveal your gender a study with 3d faces"
    - "vegac visual saliency based age gender and facial expression classification using convolutional neural networks"
    - "gender from iris or gender from mascara"
    - "can we boost the power of the viola jones face detector using pre processing an empirical study"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"
    - "factors of transferability for a generic convnet representation"

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)