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: "robust privacy utility tradeoffs under differential privacy and hamming distortion"
  Abstract: A privacy-utility tradeoff is developed for an arbitrary set of finite-alphabet source distributions. Privacy is quantified using differential privacy (DP), and utility is quantified using expected Hamming distortion maximized over the set of distributions. The family of source distribution sets (source sets) is categorized into three classes, based on different levels of prior knowledge they capture. For source sets whose convex hull includes the uniform distribution, symmetric DP mechanisms are optimal. For source sets whose probability values have a fixed monotonic ordering, asymmetric DP mechanisms are optimal. For all other source sets, general upper and lower bounds on the optimal privacy leakage are developed and necessary and sufficient conditions for tightness are established. Differentially private leakage is an upper bound on mutual information leakage: the two criteria are compared analytically and numerically to illustrate the effect of adopting a stronger privacy criterion.

Connected through hyperedge HE-1 (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"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "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-2 (induced by source paper "interactive secure function computation"):
  with members:
    - "communication rounds tradeoffs for common randomness and secret key generation"
    - "secret key agreement under discussion rate constraints"
    - "communication and randomness lower bounds for secure computation"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "information complexity density and simulation of protocols"
    - "secure multiterminal source coding with side information at the eavesdropper"
    - "distributed channel synthesis"
    - "common information and secret key capacity"

Connected through hyperedge HE-3 (induced by source paper "decentralized detection with robust information privacy protection"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "multilayer nonlinear processing for information privacy in sensor networks"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "big data privacy in the internet of things era"
    - "information extraction under privacy constraints"

Connected through hyperedge HE-4 (induced by source paper "matching anonymized and obfuscated time series to users profiles"):
  with members:
    - "towards provably invisible network flow fingerprints"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "information theoretic privacy with general distortion constraints"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "where you are is who you are user identification by matching statistics"
    - "achieving perfect location privacy in wireless devices using anonymization"
    - "hypothesis testing in the high privacy limit"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"

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)