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: "a tunable measure for information leakage"
  Abstract: A tunable measure for information leakage called \textit{maximal $\alpha$-leakage} is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of a dataset conditioning on a disclosed dataset. The choice of $\alpha$ determines the specific adversarial action ranging from refining a belief for $\alpha =1$ to guessing the best posterior for $\alpha = \infty$, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. For all other $\alpha$ this measure is shown to be the Arimoto channel capacity. Several properties of this measure are proven including: (i) quasi-convexity in the mapping between the original and disclosed datasets; (ii) data processing inequalities; and (iii) a composition property.

Connected through hyperedge HE-1 (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"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-2 (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"
    - "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"
    - "context aware generative adversarial privacy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "universal mutual information privacy guarantees for smart meters"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "joint privacy cost optimization in smart electricity metering systems"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "smart meter privacy via the trapdoor channel"
    - "smart meter privacy with renewable energy and a finite capacity battery"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"
    - "smart meter privacy a utility privacy framework"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "privacy against brute force inference attacks"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "multi user guesswork and brute force security"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"
    - "guessing revisited a large deviations approach"

Connected through hyperedge HE-5 (induced by source paper "robustness of maximal alpha leakage to side information"):
  with members:
    - "an operational approach to information leakage"
    - "privacy aware guessing efficiency"
    - "renyi differential privacy"
    - "on the semantics of differential privacy a bayesian formulation"

Connected through hyperedge HE-6 (induced by source paper "a tunable loss function for binary classification"):
  with members:
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "on loss functions for deep neural networks in classification"

Connected through hyperedge HE-7 (induced by source paper "on the robustness of information theoretic privacy measures and mechanisms"):
  with members:
    - "data disclosure under perfect sample privacy"
    - "privacy against brute force inference attacks"
    - "estimation efficiency under privacy constraints"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "the utility cost of robust privacy guarantees"
    - "privacy under hard distortion constraints"
    - "technical privacy metrics a systematic survey"

Connected through hyperedge HE-8 (induced by source paper "optimal utility privacy trade off with the total variation distance as the privacy measure"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "the utility cost of robust privacy guarantees"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "privacy preserving adversarial networks"
    - "privacy aware guessing efficiency"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "an estimation theoretic view of privacy"
    - "privacy aware mmse estimation"

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.IT(Information Theory)