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: "an end to end system for unconstrained face verification with deep convolutional neural networks"
  Abstract: Over the last four years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this paper, we present the design details of a deep learning system for end-to-end unconstrained face verification/recognition. The quantitative performance evaluation is conducted using the newly released IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the LFW dataset. The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the Labeled Faces in the Wild (LFW) and Youtube Face (YTF) datasets. JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos for evaluating video-based face verification system. Some open issues regarding DCNNs for object recognition problems are then discussed.

Connected through hyperedge HE-1 (induced by source paper "regula sub rosa latent backdoor attacks on deep neural networks"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "adversarial patch"
    - "adversarial logit pairing"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "spectral signatures in backdoor attacks"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-2 (induced by source paper "dependency aware attention control for unconstrained face recognition with image sets"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep reinforcement learning a brief survey"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "quality aware network for set to set recognition"
    - "deep reinforcement learning an overview"
    - "deep reinforcement learning attention selection for person re identification"
    - "classification with costly features using deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "compositional hierarchical tensor factorization representing hierarchical intrinsic and extrinsic causal factors"):
  with members:
    - "tensor contraction layers for parsimonious deep nets"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "sparse hierarchical tucker factorization and its application to healthcare"
    - "tensorizing neural networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "theano new features and speed improvements"

Connected through hyperedge HE-4 (induced by source paper "pairwise relational networks for face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "faceposenet making a case for landmark free face alignment"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "face recognition using deep multi pose representations"

Connected through hyperedge HE-5 (induced by source paper "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"):
  with members:
    - "neural aggregation network for video face recognition"
    - "a lightened cnn for deep face representation"

Connected through hyperedge HE-6 (induced by source paper "unconstrained still video based face verification with deep convolutional neural networks"):
  with members:
    - "deep heterogeneous feature fusion for template based face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "do we really need to collect millions of faces for effective face recognition"
    - "face alignment by local deep descriptor regression"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

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)