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: "linear disentangled representation learning for facial actions"
  Abstract: Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features. However, a linear model with just several parameters normally is not demanding in terms of training data. In this paper, we propose an elegant linear model to untangle confounding factors in challenging realistic multichannel signals such as 2D face videos. The simple yet powerful model does not rely on huge training data and is natural for recognizing facial actions without explicitly disentangling the identity. Base on well-understood intuitive linear models such as Sparse Representation based Classification (SRC), previous attempts require a prepossessing of explicit decoupling which is practically inexact. Instead, we exploit the low-rank property across frames to subtract the underlying neutral faces which are modeled jointly with sparse representation on the action components with group sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot automatic method on raw face videos performs as competitive as SRC applied on manually prepared action components and performs even better than SRC in terms of true positive rate. We apply the model to the even more challenging task of facial action unit recognition, verified on the MPI Face Video Database (MPI-VDB) achieving a decent performance. All the programs and data have been made publicly available.

Connected through hyperedge HE-1 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "k beam minimax efficient optimization for deep adversarial learning"
    - "image to image translation for cross domain disentanglement"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "adversarial image perturbation for privacy protection a game theory perspective"

Connected through hyperedge HE-3 (induced by source paper "towards privacy preserving visual recognition via adversarial training a pilot study"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "learning transferable architectures for scalable image recognition"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "adversarial image perturbation for privacy protection a game theory perspective"

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)