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: "utility privacy tradeoffs in databases an information theoretic approach"
  Abstract: Ensuring the usefulness of electronic data sources while providing necessary privacy guarantees is an important unsolved problem. This problem drives the need for an analytical framework that can quantify the privacy of personally identifiable information while still providing a quantifiable benefit (utility) to multiple legitimate information consumers. This paper presents an information-theoretic framework that promises an analytical model guaranteeing tight bounds of how much utility is possible for a given level of privacy and vice-versa. Specific contributions include: 1) stochastic data models for both categorical and numerical data; 2) utility-privacy tradeoff regions and the encoding (sanization) schemes achieving them for both classes and their practical relevance; and 3) modeling of prior knowledge at the user and/or data source and optimal encoding schemes for both cases.

Connected through hyperedge HE-1 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "an estimation theoretic view of privacy"
    - "on maximal correlation mutual information and data privacy"
    - "maximal correlation secrecy"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"
    - "smart meter privacy a utility privacy framework"

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 "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-4 (induced by source paper "shredder learning noise to protect privacy with partial dnn inference on the edge"):
  with members:
    - "zombieload cross privilege boundary data sampling"
    - "chiron privacy preserving machine learning as a service"
    - "spectre attacks exploiting speculative execution"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-5 (induced by source paper "estimation efficiency under privacy constraints"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "generalizing bottleneck problems"
    - "privacy aware guessing efficiency"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "privacy aware mmse estimation"
    - "notes on information theoretic privacy"

Connected through hyperedge HE-6 (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"
    - "context aware generative adversarial privacy"
    - "privacy preserving personal model training"

Connected through hyperedge HE-7 (induced by source paper "noiseless privacy"):
  with members:
    - "an operational approach to information leakage"
    - "uncertain wiretap channels and secure estimation"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "extremal mechanisms for local differential privacy"
    - "a nonstochastic information theory for communication and state estimation"
    - "the composition theorem for differential privacy"
    - "information masking and amplification the source coding setting"
    - "privacy against statistical inference"

Connected through hyperedge HE-8 (induced by source paper "development and analysis of deterministic privacy preserving policies using non stochastic information theory"):
  with members:
    - "uncertain wiretap channels and secure estimation"
    - "extremal mechanisms for local differential privacy"
    - "a nonstochastic information theory for communication and state estimation"
    - "information masking and amplification the source coding setting"
    - "the optimal mechanism in differential privacy"
    - "privacy against statistical inference"
    - "on the relation between differential privacy and quantitative information flow"

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)