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: "decorrelated adversarial learning for age invariant face recognition"
  Abstract: There has been an increasing research interest in age-invariant face recognition. However, matching faces with big age gaps remains a challenging problem, primarily due to the significant discrepancy of face appearances caused by aging. To reduce such a discrepancy, in this paper we propose a novel algorithm to remove age-related components from features mixed with both identity and age information. Specifically, we factorize a mixed face feature into two uncorrelated components: identity-dependent component and age-dependent component, where the identity-dependent component includes information that is useful for face recognition. To implement this idea, we propose the Decorrelated Adversarial Learning (DAL) algorithm, where a Canonical Mapping Module (CMM) is introduced to find the maximum correlation between the paired features generated by a backbone network, while the backbone network and the factorization module are trained to generate features reducing the correlation. Thus, the proposed model learns the decomposed features of age and identity whose correlation is significantly reduced. Simultaneously, the identity-dependent feature and the age-dependent feature are respectively supervised by ID and age preserving signals to ensure that they both contain the correct information. Extensive experiments are conducted on popular public-domain face aging datasets (FG-NET, MORPH Album 2, and CACD-VS) to demonstrate the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (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"
    - "mvf net multi view 3d face morphable model regression"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "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-2 (induced by source paper "child face age progression via deep feature aging"):
  with members:
    - "orthogonal deep features decomposition 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-3 (induced by source paper "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "illumination adaptive person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "gait recognition via disentangled representation learning"
    - "re identification with consistent attentive siamese networks"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"

Connected through hyperedge HE-4 (induced by source paper "efficient decision based black box adversarial attacks on face recognition"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "orthogonal deep features decomposition 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-5 (induced by source paper "finding missing children aging deep face features"):
  with members:
    - "how are attributes expressed in face dcnns"
    - "orthogonal deep features decomposition 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-6 (induced by source paper "occlusion robust face recognition based on mask learning with pairwisedifferential siamese network"):
  with members:
    - "orthogonal deep features decomposition 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"

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)