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: "managing your private and public data bringing down inference attacks against your privacy"
  Abstract: We propose a practical methodology to protect a user's private data, when he wishes to publicly release data that is correlated with his private data, to get some utility. Our approach relies on a general statistical inference framework that captures the privacy threat under inference attacks, given utility constraints. Under this framework, data is distorted before it is released, according to a probabilistic privacy mapping. This mapping is obtained by solving a convex optimization problem, which minimizes information leakage under a distortion constraint. We address practical challenges encountered when applying this theoretical framework to real world data. On one hand, the design of optimal privacy mappings requires knowledge of the prior distribution linking private data and data to be released, which is often unavailable in practice. On the other hand, the optimization may become untractable when data assumes values in large size alphabets, or is high dimensional. Our work makes three major contributions. First, we provide bounds on the impact of a mismatched prior on the privacy-utility tradeoff. Second, we show how to reduce the optimization size by introducing a quantization step, and how to generate privacy mappings under quantization. Third, we evaluate our method on two datasets, including a new dataset that we collected, showing correlations between political convictions and TV viewing habits. We demonstrate that good privacy properties can be achieved with limited distortion so as not to undermine the original purpose of the publicly released data, e.g., recommendations.

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"
    - "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-2 (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-3 (induced by source paper "information theoretic privacy through chaos synchronization and optimal additive noise"):
  with members:
    - "on privacy of quantized sensor measurements through additive noise"
    - "privacy constrained information processing"
    - "increasing smart meter privacy through energy harvesting and storage devices"
    - "the optimal mechanism in differential privacy"
    - "privacy against statistical inference"
    - "smart meter privacy a utility privacy framework"

Connected through hyperedge HE-4 (induced by source paper "on privacy of quantized sensor measurements through additive noise"):
  with members:
    - "on reachable sets of hidden cps sensor attacks"
    - "constraining attacker capabilities through actuator saturation"
    - "a comparison of stealthy sensor attacks on control systems"
    - "increasing smart meter privacy through energy harvesting and storage devices"
    - "the optimal mechanism in differential privacy"
    - "privacy against statistical inference"

Connected through hyperedge HE-5 (induced by source paper "context aware generative adversarial privacy"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "information theoretic privacy with general distortion constraints"
    - "ensemble estimation of mutual information"
    - "deeprotect enabling inference based access control on mobile sensing applications"
    - "information potential auto encoders"
    - "privacy aware mmse estimation"
    - "minimax filter learning to preserve privacy from inference attacks"

Connected through hyperedge HE-6 (induced by source paper "principal inertia components and applications"):
  with members:
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "the most informative boolean function conjecture holds for high noise"
    - "bounds between contraction coefficients"
    - "on the duality of additivity and tensorization"
    - "remarks on the most informative function conjecture at fixed mean"
    - "euclidean information theory of networks"
    - "notes on information theoretic privacy"
    - "an improved upper bound for the most informative boolean function conjecture"

Connected through hyperedge HE-7 (induced by source paper "towards information privacy for the internet of things"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "privacy in the internet of things threats and challenges"
    - "distributed localization of a rf target in nlos environments"
    - "geo indistinguishability differential privacy for location based systems"
    - "privacy against statistical inference"
    - "the value of feedback in decentralized detection"
    - "differentially private empirical risk minimization"

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.CR(Cryptography and Security)