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: "a good practice towards top performance of face recognition transferred deep feature fusion"
  Abstract: Unconstrained face recognition performance evaluations have traditionally focused on Labeled Faces in the Wild (LFW) dataset for imagery and the YouTubeFaces (YTF) dataset for videos in the last couple of years. Spectacular progress in this field has resulted in saturation on verification and identification accuracies for those benchmark datasets. In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder than the LFW and YTF datasets. Inspired by transfer learning, we train two advanced deep convolutional neural networks (DCNN) with two different large datasets in source domain, respectively. By exploring the complementarity of two distinct DCNNs, deep feature fusion is utilized after feature extraction in target domain. Then, template specific linear SVMs is adopted to enhance the discrimination of framework. Finally, multiple matching scores corresponding different templates are merged as the final results. This simple unified framework exhibits excellent performance on IJB-A dataset. Based on the proposed approach, we have submitted our IJB-A results to National Institute of Standards and Technology (NIST) for official evaluation. Moreover, by introducing new data and advanced neural architecture, our method outperforms the state-of-the-art by a wide margin on IJB-A dataset.

Connected through hyperedge HE-1 (induced by source paper "crystal loss and quality pooling for unconstrained face verification and recognition"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "the do s and don ts for cnn based face verification"
    - "a proximity aware hierarchical clustering of faces"
    - "faceposenet making a case for landmark free face alignment"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "supervised cosmos autoencoder learning beyond the euclidean loss"):
  with members:
    - "residual codean autoencoder for facial attribute analysis"
    - "semantic autoencoder for zero shot learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "relational autoencoder for feature extraction"
    - "improving facial attribute prediction using semantic segmentation"
    - "dynamic routing between capsules"
    - "age progression regression by conditional adversarial autoencoder"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

Connected through hyperedge HE-3 (induced by source paper "docface matching id document photos to selfies"):
  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"
    - "large scale bisample learning on id vs spot face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deepvisage making face recognition simple yet with powerful generalization skills"

Connected through hyperedge HE-4 (induced by source paper "l2 constrained softmax loss for discriminative face verification"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "a proximity aware hierarchical clustering of faces"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "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"
    - "aggregated residual transformations for deep neural networks"
    - "deep convolutional neural network features and the original image"

Connected through hyperedge HE-5 (induced by source paper "darkrank accelerating deep metric learning via cross sample similarities transfer"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "sobolev training for neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "densely connected convolutional networks"
    - "learning both weights and connections for efficient neural networks"
    - "unsupervised learning of visual representations using videos"

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"
    - "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"
    - "a fast and accurate unconstrained face detector"

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)