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: "convolutional neural networks for attribute based active authentication on mobile devices"
  Abstract: We present a Deep Convolutional Neural Network (DCNN) architecture for the task of continuous authentication on mobile devices. To deal with the limited resources of these devices, we reduce the complexity of the networks by learning intermediate features such as gender and hair color instead of identities. We present a multi-task, part-based DCNN architecture for attribute detection that performs better than the state-of-the-art methods in terms of accuracy. As a byproduct of the proposed architecture, we are able to explore the embedding space of the attributes extracted from different facial parts, such as mouth and eyes, to discover new attributes. Furthermore, through extensive experimentation, we show that the attribute features extracted by our method outperform the previously presented attribute-based method and a baseline LBP method for the task of active authentication. Lastly, we demonstrate the effectiveness of the proposed architecture in terms of speed and power consumption by deploying it on an actual mobile device.

Connected through hyperedge HE-1 (induced by source paper "a quadruplet loss for enforcing semantically coherent embeddings in multi output classification problems"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "attention set based metric learning for video face recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "ssd single shot multibox detector"
    - "facenet a unified embedding for face recognition and clustering"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-2 (induced by source paper "learning deep features for one class classification"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "latent space autoregression for novelty detection"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "sparse representation based open set recognition"

Connected through hyperedge HE-3 (induced by source paper "active authentication using an autoencoder regularized cnn based one class classifier"):
  with members:
    - "one class convolutional neural network"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "anomaly detection using one class neural networks"
    - "abnormal event detection in videos using generative adversarial nets"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "a learned representation for artistic style"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "robust rgb d face recognition using attribute aware loss"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "learning from millions of 3d scans for large scale 3d face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deep 3d face identification"

Connected through hyperedge HE-5 (induced by source paper "partial face detection in the mobile domain"):
  with members:
    - "pooling facial segments to face the shallow and deep ends"
    - "deep feature based face detection on mobile devices"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"
    - "partial face detection for continuous authentication"

Connected through hyperedge HE-6 (induced by source paper "pooling facial segments to face the shallow and deep ends"):
  with members:
    - "deep feature based face detection on mobile devices"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "a fast and accurate unconstrained face detector"
    - "partial face detection for continuous authentication"
    - "an all in one convolutional neural network for face analysis"
    - "multi view face detection using deep convolutional neural networks"
    - "a deep pyramid deformable part model for face detection"

Connected through hyperedge HE-7 (induced by source paper "enlightening deep neural networks with knowledge of confounding factors"):
  with members:
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "deep convolutional networks are hierarchical kernel machines"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep convolutional inverse graphics network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)