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: "one to many face recognition with bilinear cnns"
  Abstract: The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bilinear CNN (B-CNN), which has shown dramatic performance gains on certain fine-grained recognition problems [15]. We apply this new CNN to the challenging new face recognition benchmark, the IARPA Janus Benchmark A (IJB-A) [12]. It features faces from a large number of identities in challenging real-world conditions. Because the face images were not identified automatically using a computerized face detection system, it does not have the bias inherent in such a database. We demonstrate the performance of the B-CNN model beginning from an AlexNet-style network pre-trained on ImageNet. We then show results for fine-tuning using a moderate-sized and public external database, FaceScrub [17]. We also present results with additional fine-tuning on the limited training data provided by the protocol. In each case, the fine-tuned bilinear model shows substantial improvements over the standard CNN. Finally, we demonstrate how a standard CNN pre-trained on a large face database, the recently released VGG-Face model [20], can be converted into a B-CNN without any additional feature training. This B-CNN improves upon the CNN performance on the IJB-A benchmark, achieving 89.5% rank-1 recall.

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 "multi prototype networks for unconstrained set based face recognition"):
  with members:
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "cosface large margin cosine loss for deep face recognition"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for 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"

Connected through hyperedge HE-3 (induced by source paper "attentional feature pair relation networks for accurate face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "pairwise relational networks for face recognition"
    - "comparator networks"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "few shot learning with localization in realistic settings"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "low shot learning from imaginary data"
    - "power normalizing second order similarity network for few shot learning"
    - "prototypical networks for few shot learning"
    - "extreme clicking for efficient object annotation"
    - "a simple neural attentive meta learner"
    - "residual attention network for image classification"

Connected through hyperedge HE-5 (induced by source paper "racial faces in the wild reducing racial bias by information maximization adaptation network"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "deep visual domain adaptation a survey"
    - "arcface additive angular margin loss for deep face recognition"
    - "supervised cosmos autoencoder learning beyond the euclidean loss"
    - "deep face recognition a survey"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-6 (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"
    - "template adaptation for face verification and identification"
    - "viplfacenet an open source deep face recognition sdk"

Connected through hyperedge HE-7 (induced by source paper "trunk branch ensemble convolutional neural networks for video based face recognition"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "fine grained pose prediction normalization and recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "deepid3 face recognition with very deep neural networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (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"

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)