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: "context aware generative adversarial privacy"
  Abstract: Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals’ private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP’s performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model; and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

Connected through hyperedge HE-1 (induced by source paper "local information privacy and its application to privacy preserving data aggregation"):
  with members:
    - "context aware data aggregation with localized information privacy"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "privacy aware mmse estimation"
    - "differential privacy as a mutual information constraint"
    - "concentrated differential privacy"
    - "privacy preserving analysis of correlated data"
    - "on maximal correlation mutual information and data privacy"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving image sharing via sparsifying layers on convolutional groups"):
  with members:
    - "learning to compress and search visual data in large scale systems"
    - "generative adversarial networks for extreme learned image compression"
    - "encryption then compression systems using grayscale based image encryption for jpeg images"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "defeating image obfuscation with deep learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (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"
    - "generative adversarial privacy"
    - "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"

Connected through hyperedge HE-4 (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"
    - "generative adversarial privacy"
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"

Connected through hyperedge HE-5 (induced by source paper "adversarial task specific privacy preservation under attribute attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning adversarially fair and transferable representations"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "towards robust and privacy preserving text representations"
    - "privacy preserving distributed deep learning for clinical data"
    - "data decisions and theoretical implications when adversarially learning fair representations"

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 "a class of parameterized loss functions for classification optimization tradeoffs and robustness characteristics"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "a tunable loss function for binary classification"
    - "a tunable measure for information leakage"
    - "rademacher complexity for adversarially robust generalization"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "mobile sensor data anonymization"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "distributed one class learning"
    - "protecting sensory data against sensitive inferences"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "privacy preserving adversarial networks"
    - "privacy preserving personal model training"
    - "deeprotect enabling inference based access control on mobile sensing applications"

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)