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: "funnel structured cascade for multi view face detection with alignment awareness"
  Abstract: Multi-view face detection in open environment is a challenging task due to diverse variations of face appearances and shapes. Most multi-view face detectors depend on multiple models and organize them in parallel, pyramid or tree structure, which compromise between the accuracy and time-cost. Aiming at a more favorable multi-view face detector, we propose a novel funnel-structured cascade (FuSt) detection framework. In a coarse-to-fine flavor, our FuSt consists of, from top to bottom, (1) multiple view-specific fast LAB cascade for extremely quick face proposal, (2) multiple coarse MLP cascade for further candidate window verification, and (3) a unified fine MLP cascade with shape-indexed features for accurate face detection. Compared with other structures, on the one hand, the proposed one uses multiple computationally efficient distributed classifiers to propose a small number of candidate windows but with a high recall of multi-view faces. On the other hand, by using a unified MLP cascade to examine proposals of all views in a centralized style, it provides a favorable solution for multi-view face detection with high accuracy and low timecost. Besides, the FuSt detector is alignment-aware and performs a coarse facial part prediction which is beneficial for subsequent face alignment. Extensive experiments on two challenging datasets, FDDB and AFW, demonstrate the effectiveness of our FuSt detector in both accuracy and speed.

Connected through hyperedge HE-1 (induced by source paper "towards real time eyeblink detection in the wild dataset theory and practices"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "high speed tracking with kernelized correlation filters"
    - "adam a method for stochastic optimization"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-2 (induced by source paper "exponential discriminative metric embedding in deep learning"):
  with members:
    - "binary generative adversarial networks for image retrieval"
    - "learning in high dimensional multimedia data the state of the art"
    - "a survey on learning to hash"
    - "wide residual networks"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"
    - "unconstrained face verification using deep cnn features"

Connected through hyperedge HE-3 (induced by source paper "identity enhanced network for facial expression recognition"):
  with members:
    - "focal loss for dense object detection"
    - "deep generative contrastive networks for facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "densely connected convolutional networks"
    - "peak piloted deep network for facial expression recognition"
    - "learning face representation from scratch"

Connected through hyperedge HE-4 (induced by source paper "multi modal face pose estimation with multi task manifold deep learning"):
  with members:
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "smoothing proximal gradient method for general structured sparse learning"

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)