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: "orthogonal deep features decomposition for age invariant face recognition"
  Abstract: As facial appearance is subject to significant intra-class variations caused by the aging process over time, age-invariant face recognition (AIFR) remains a major challenge in face recognition community. To reduce the intra-class discrepancy caused by the aging, in this paper we propose a novel approach (namely, Orthogonal Embedding CNNs, or OE-CNNs) to learn the age-invariant deep face features. Specifically, we decompose deep face features into two orthogonal components to represent age-related and identity-related features. As a result, identity-related features that are robust to aging are then used for AIFR. Besides, for complementing the existing cross-age datasets and advancing the research in this field, we construct a brand-new large-scale Cross-Age Face dataset (CAF). Extensive experiments conducted on the three public domain face aging datasets (MORPH Album 2, CACD-VS and FG-NET) have shown the effectiveness of the proposed approach and the value of the constructed CAF dataset on AIFR. Benchmarking our algorithm on one of the most popular general face recognition (GFR) dataset LFW additionally demonstrates the comparable generalization performance on GFR.

Connected through hyperedge HE-1 (induced by source paper "learning meta face recognition in unseen domains"):
  with members:
    - "meta learning with implicit gradients"
    - "attentional feature pair relation networks for accurate face recognition"
    - "on first order meta learning algorithms"
    - "face synthesis for eyeglass robust face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "meta transfer learning for few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"

Connected through hyperedge HE-2 (induced by source paper "facial attribute capsules for noise face super resolution"):
  with members:
    - "occlusion robust face recognition based on mask learning with pairwisedifferential siamese network"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "mvf net multi view 3d face morphable model regression"
    - "arcface additive angular margin loss for deep face recognition"
    - "residual dense network for image super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "child face age progression via deep feature aging"):
  with members:
    - "decorrelated adversarial learning for age invariant face recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "face aging with conditional generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"

Connected through hyperedge HE-4 (induced by source paper "decorrelated adversarial learning for age invariant face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "exploring disentangled feature representation beyond face identification"
    - "additive margin softmax for face verification"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-5 (induced by source paper "efficient decision based black box adversarial attacks on face recognition"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-6 (induced by source paper "finding missing children aging deep face features"):
  with members:
    - "how are attributes expressed in face dcnns"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-7 (induced by source paper "occlusion robust face recognition based on mask learning with pairwisedifferential siamese network"):
  with members:
    - "decorrelated adversarial learning for age invariant face recognition"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

Connected through hyperedge HE-8 (induced by source paper "minimum margin loss for deep face recognition"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "cosface large margin cosine loss for deep face recognition"
    - "side information for face completion a robust pca approach"
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "ring loss convex feature normalization for face recognition"
    - "additive margin softmax for face verification"
    - "comparator networks"

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)