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: "unconstrained still video based face verification with deep convolutional neural networks"
  Abstract: Over the last five 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 unconstrained face recognition, including modules for face detection, association, alignment and face verification. The quantitative performance evaluation is conducted using the 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 the video-based face verification system. Some open issues regarding DCNNs for face verification problems are then discussed.

Connected through hyperedge HE-1 (induced by source paper "deep learning for face recognition a critical analysis"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face attention network an effective face detector for the occluded faces"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "the do s and don ts for cnn based face verification"

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 "recurrent embedding aggregation network for video face recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "multi prototype networks for unconstrained set based face recognition"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "ghostvlad for set based face recognition"
    - "multicolumn networks for face recognition"

Connected through hyperedge HE-4 (induced by source paper "face recognition in unconstrained conditions a systematic review"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "face detection through scale friendly deep convolutional networks"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"

Connected through hyperedge HE-5 (induced by source paper "uncertainty modeling of contextual connection between tracklets for unconstrained video based face recognition"):
  with members:
    - "recurrent embedding aggregation network for video face recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "person search in videos with one portrait through visual and temporal links"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "an automatic system for unconstrained video based face recognition"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-6 (induced by source paper "video face recognition component wise feature aggregation network c fan"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "quality aware network for set to set recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deepvisage making face recognition simple yet with powerful generalization skills"

Connected through hyperedge HE-7 (induced by source paper "occlusion guided compact template learning for ensemble deep network based pose invariant face recognition"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "on the intrinsic dimensionality of face representation"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "covariance free partial least squares an incremental dimensionality reduction method"):
  with members:
    - "pruning deep neural networks using partial least squares"
    - "learning multiple visual domains with residual adapters"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "fast r cnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual 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)