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: "learning deep face representation"
  Abstract: Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy to implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. A very easy-to-implement deep learning framework for face representation is presented. The framework bases on pyramid convolutional neural network (CNN). The pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation.

Connected through hyperedge HE-1 (induced by source paper "quadruplet network with one shot learning for fast visual object tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "large margin object tracking with circulant feature maps"
    - "deep reinforcement learning for visual object tracking in videos"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "learning feed forward one shot learners"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-2 (induced by source paper "improving siamese networks for one shot learning using kernel based activation functions"):
  with members:
    - "video summarization using keyframe extraction and video skimming"
    - "learning feed forward one shot learners"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "face recognition from traditional to deep learning methods"):
  with members:
    - "representation learning by rotating your faces"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-4 (induced by source paper "query free attacks on industry grade face recognition systems under resource constraints"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial attacks on neural network policies"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "upset and angri breaking high performance image classifiers"
    - "mixup beyond empirical risk minimization"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-5 (induced by source paper "how far did we get in face spoofing detection"):
  with members:
    - "deep representations for iris face and fingerprint spoofing detection"
    - "face anti spoofing based on color texture analysis"

Connected through hyperedge HE-6 (induced by source paper "3d face hallucination from a single depth frame"):
  with members:
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-7 (induced by source paper "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "triplet probabilistic embedding for face verification and clustering"
    - "triplet similarity embedding for face verification"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "learning descriptors for object recognition and 3d pose estimation"

Connected through hyperedge HE-8 (induced by source paper "white man and highly followed gender and race inequalities in twitter"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "demographics of news sharing in the u s twittersphere"
    - "linguistic diversities of demographic groups in twitter"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "from migration corridors to clusters the value of google data for migration studies"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"

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)