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: "face detection with effective feature extraction"
  Abstract: There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.

Connected through hyperedge HE-1 (induced by source paper "compare more nuanced pairwise alignment bilinear network for few shot fine grained learning"):
  with members:
    - "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "cross modal hallucination for few shot fine grained recognition"
    - "one shot fine grained instance retrieval"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-2 (induced by source paper "compact convolutional neural network cascade for face detection"):
  with members:
    - "a fast and accurate unconstrained face detector"
    - "face detection with a 3d model"
    - "multi view face detection using deep convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "from facial parts responses to face detection a deep learning approach"
    - "going deeper with convolutions"
    - "densenet implementing efficient convnet descriptor pyramids"
    - "fast convolutional nets with fbfft a gpu performance evaluation"

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)