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: "minimax filter learning to preserve privacy from inference attacks"
  Abstract: Preserving privacy of continuous and/or high-dimensional data such as images, videos and audios, can be challenging with syntactic anonymization methods which are designed for discrete attributes. Differential privacy, which provides a more formal definition of privacy, has shown more success in sanitizing continuous data. However, both syntactic and differential privacy are susceptible to inference attacks, i.e., an adversary can accurately infer sensitive attributes from sanitized data. The paper proposes a novel filter-based mechanism which preserves privacy of continuous and high-dimensional attributes against inference attacks. Finding the optimal utility-privacy tradeoff is formulated as a min-diff-max optimization problem. The paper provides an ERM-like analysis of the generalization error and also a practical algorithm to perform the optimization. In addition, the paper proposes an extension that combines minimax filter and differentially-private noisy mechanism. Advantages of the method over purely noisy mechanisms is explained and demonstrated with examples. Experiments with several real-world tasks including facial expression classification, speech emotion classification, and activity classification from motion, show that the minimax filter can simultaneously achieve similar or better target task accuracy and lower inference accuracy, often significantly lower than previous methods.

Connected through hyperedge HE-1 (induced by source paper "addressing target shift in zero shot learning using grouped adversarial learning"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "detecting and correcting for label shift with black box predictors"
    - "mitigating unwanted biases with adversarial learning"
    - "semantic autoencoder for zero shot learning"
    - "adversarial discriminative domain adaptation"
    - "controllable invariance through adversarial feature learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "label embedding for image classification"

Connected through hyperedge HE-2 (induced by source paper "an adversarial learning framework for preserving users anonymity in face based emotion recognition"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "privacy preserving human activity recognition from extreme low resolution"

Connected through hyperedge HE-3 (induced by source paper "revealing scenes by inverting structure from motion reconstructions"):
  with members:
    - "privacy preserving image based localization"
    - "learning privacy preserving encodings through adversarial training"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "distributed generation of privacy preserving data with user customization"
    - "privacy under hard distortion constraints"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "fairness in supervised learning an information theoretic approach"
    - "fairgan fairness aware generative adversarial networks"
    - "learning to protect communications with adversarial neural cryptography"

Connected through hyperedge HE-5 (induced by source paper "contamination attacks and mitigation in multi party machine learning"):
  with members:
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "an algorithmic framework for differentially private data analysis on trusted processors"
    - "is feature selection secure against training data poisoning"
    - "understanding membership inferences on well generalized learning models"
    - "logan membership inference attacks against generative models"
    - "learning differentially private recurrent language models"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "machine learning models that remember too much"

Connected through hyperedge HE-6 (induced by source paper "distributed generation of privacy preserving data with user customization"):
  with members:
    - "understanding compressive adversarial privacy"
    - "protecting sensitive attributes via generative adversarial networks"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "learning to protect communications with adversarial neural cryptography"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-7 (induced by source paper "inherent tradeoffs in learning fair representations"):
  with members:
    - "conditional learning of fair representations"
    - "adversarial privacy preservation under attribute inference attack"
    - "on learning invariant representation for domain adaptation"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"

Connected through hyperedge HE-8 (induced by source paper "adversarial privacy preservation under attribute inference attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "privacy preserving distributed deep learning for clinical data"
    - "age progression regression by conditional adversarial autoencoder"
    - "equality of opportunity in supervised learning"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
    - "wide residual 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.LG(Machine Learning)