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: "trunk branch ensemble convolutional neural networks for video based face recognition"
  Abstract: Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically. Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components. TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low- and middle-level convolutional layers between the trunk and branch networks. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. Systematic experiments justify the effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces. With the proposed techniques, we also obtain the first place in the BTAS 2016 Video Person Recognition Evaluation.

Connected through hyperedge HE-1 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "self supervised learning of face representations for video face clustering"
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "sspp dan deep domain adaptation network for face recognition with single sample per person"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-2 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "video person re identification by temporal residual learning"
    - "person re identification with metric learning using privileged information"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning view specific deep networks for person re identification"

Connected through hyperedge HE-3 (induced by source paper "dependency aware attention control for unconstrained face recognition with image sets"):
  with members:
    - "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"
    - "multi shot pedestrian re identification via sequential decision making"

Connected through hyperedge HE-4 (induced by source paper "attention control with metric learning alignment for image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "conservative wasserstein training for pose estimation"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "revisiting temporal modeling for video based person reid"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "learning policies for adaptive tracking with deep feature cascades"

Connected through hyperedge HE-5 (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"
    - "ghostvlad for set based face recognition"
    - "multicolumn networks for face recognition"
    - "comparator networks"

Connected through hyperedge HE-6 (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"
    - "an automatic system for unconstrained video based face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-7 (induced by source paper "asynchronous federated learning with differential privacy for edge intelligence"):
  with members:
    - "differentially private learning with adaptive clipping"
    - "min entropy latent model for weakly supervised object detection"
    - "federated machine learning concept and applications"
    - "interpret federated learning with shapley values"
    - "exploiting unintended feature leakage in collaborative learning"
    - "mvg mechanism differential privacy under matrix valued query"
    - "federated learning strategies for improving communication efficiency"
    - "a general approach to adding differential privacy to iterative training procedures"

Connected through hyperedge HE-8 (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"
    - "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"
    - "neural aggregation network for video face 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)