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: "learning privacy preserving encodings through adversarial training"
  Abstract: We present a framework to learn privacy-preserving encodings of images that inhibit inference of chosen private attributes, while allowing recovery of other desirable information. Rather than simply inhibiting a given fixed pre-trained estimator, our goal is that an estimator be unable to learn to accurately predict the private attributes even with knowledge of the encoding function. We use a natural adversarial optimization-based formulation for this---training the encoding function against a classifier for the private attribute, with both modeled as deep neural networks. The key contribution of our work is a stable and convergent optimization approach that is successful at learning an encoder with our desired properties---maintaining utility while inhibiting inference of private attributes, not just within the adversarial optimization, but also by classifiers that are trained after the encoder is fixed. We adopt a rigorous experimental protocol for verification wherein classifiers are trained exhaustively till saturation on the fixed encoders. We evaluate our approach on tasks of real-world complexity---learning high-dimensional encodings that inhibit detection of different scene categories---and find that it yields encoders that are resilient at maintaining privacy.

Connected through hyperedge HE-1 (induced by source paper "revealing scenes by inverting structure from motion reconstructions"):
  with members:
    - "privacy preserving image based localization"
    - "deep multi scale architectures for monocular depth estimation"
    - "semi parametric image synthesis"
    - "megadepth learning single view depth prediction from internet photos"
    - "image manipulation with perceptual discriminators"
    - "estimating depth from rgb and sparse sensing"
    - "smart sparse contours to represent and edit images"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial learning of privacy preserving and task oriented representations"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "knockoff nets stealing functionality of black box models"
    - "learning to anonymize faces for privacy preserving action detection"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "faceless person recognition privacy implications in social media"
    - "deep variational information bottleneck"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-3 (induced by source paper "agnostic data debiasing through a local sanitizer learnt from an adversarial network approach"):
  with members:
    - "generating optimal privacy protection mechanisms via machine learning"
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "fairgan fairness aware generative adversarial networks"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "privacy preserving adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving action recognition using coded aperture videos"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning to anonymize faces for privacy preserving action detection"
    - "what have we learned from deep representations for action recognition"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "diffusercam lensless single exposure 3d imaging"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "playing for data ground truth from computer games"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-5 (induced by source paper "vgan based image representation learning for privacy preserving facial expression recognition"):
  with members:
    - "disentangled person image generation"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "cryptoimg privacy preserving processing over encrypted images"
    - "semi coupled two stream fusion convnets for action recognition at extremely low resolutions"
    - "semi supervised learning with deep generative models"
    - "discovering hidden factors of variation in deep networks"
    - "spontaneous expression classification in the encrypted domain"

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.LG(Machine Learning)