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: "generative adversarial privacy"
  Abstract: We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. We show that for appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. We also evaluate GAP's performance on the GENKI face database.

Connected through hyperedge HE-1 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"
    - "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"

Connected through hyperedge HE-2 (induced by source paper "obfuscation via information density estimation"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "estimation efficiency under privacy constraints"
    - "distributed generation of privacy preserving data with user customization"
    - "on the robustness of information theoretic privacy measures and mechanisms"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"
    - "breaking the bandwidth barrier geometrical adaptive entropy estimation"

Connected through hyperedge HE-4 (induced by source paper "data disclosure under perfect sample privacy"):
  with members:
    - "quantifying high order interdependencies via multivariate extensions of the mutual information"
    - "on perfect privacy and maximal correlation"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "understanding interdependency through complex information sharing"
    - "notes on information theoretic privacy"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"

Connected through hyperedge HE-5 (induced by source paper "autogan based dimension reduction for privacy preservation"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "understanding compressive adversarial privacy"
    - "cryptodl deep neural networks over encrypted data"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "continuous control with deep reinforcement learning"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"
    - "functional mechanism regression analysis under differential privacy"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving adversarial networks"):
  with members:
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "context aware generative adversarial privacy"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "censoring representations with an adversary"
    - "adam a method for stochastic optimization"

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)