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: "optimal utility privacy trade off with the total variation distance as the privacy measure"
  Abstract: Three reasons are provided in favour of $L^1$-norm as a measure of privacy-leakage: i) It is proved that this measure satisfies post-processing and linkage inequalities that make it consistent with an intuitive notion of a privacy measure; ii) It is shown that the optimal utility-privacy trade-off can be efficiently solved through a standard linear program when $L^1$-norm is employed as the privacy measure; iii) It is also proved that it is sufficient to consider this measure of privacy in order to bound the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with a bounded cost function.

Connected through hyperedge HE-1 (induced by source paper "mobile sensor data anonymization"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "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"
    - "context aware generative adversarial privacy"
    - "privacy preserving personal model training"
    - "deeprotect enabling inference based access control on mobile sensing applications"

Connected through hyperedge HE-2 (induced by source paper "tunable measures for information leakage and applications to privacy utility tradeoffs"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "robust privacy utility tradeoffs under differential privacy and hamming distortion"
    - "privacy aware guessing efficiency"
    - "renyi differential privacy"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "privacy utility tradeoffs under constrained data release mechanisms"

Connected through hyperedge HE-3 (induced by source paper "privacy under hard distortion constraints"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "a tunable measure for information leakage"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "privacy aware mmse estimation"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "privacy against statistical inference"

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)