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: "template adaptation for face verification and identification"
  Abstract: Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos. In contrast, the newly released IJB-A face recognition dataset unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for template-based face verification and identification.

Connected through hyperedge HE-1 (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-2 (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"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "neural aggregation network for video face recognition"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-3 (induced by source paper "gridface face rectification via learning local homography transformations"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "deep mean shift priors for image restoration"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "quality aware network for set to set recognition"
    - "toward end to end face recognition through alignment learning"
    - "synthesizing normalized faces from facial identity features"
    - "towards large pose face frontalization in the wild"

Connected through hyperedge HE-4 (induced by source paper "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "maximum likelihood approximate nearest neighbor method in real time image recognition"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "neural aggregation network for video face recognition"
    - "viplfacenet an open source deep face recognition sdk"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-5 (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"
    - "face recognition using deep multi pose representations"
    - "triplet probabilistic embedding for face verification and clustering"

Connected through hyperedge HE-6 (induced by source paper "ghostvlad for set based face recognition"):
  with members:
    - "multicolumn networks for face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "comparator networks"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "quality aware network for set to set recognition"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "von mises fisher mixture model based deep learning application to face verification"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "face aging with conditional generative adversarial networks"
    - "l2 constrained softmax loss for discriminative face verification"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"
    - "automatic face image quality prediction"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"

Connected through hyperedge HE-8 (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"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "neural aggregation network for video face recognition"
    - "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"

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)