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: "an information theoretic approach to privacy"
  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 overarching analytical framework that can quantify the safety of personally identifiable information (privacy) while still providing a quantifable benefit (utility) to multiple legitimate information consumers. State of the art approaches have predominantly focused on privacy. This paper presents the first information-theoretic approach that promises an analytical model guaranteeing tight bounds of how much utility is possible for a given level of privacy and vice-versa.

Connected through hyperedge HE-1 (induced by source paper "hypothesis testing under mutual information privacy constraints in the high privacy regime"):
  with members:
    - "privacy aware mmse estimation"
    - "hypothesis testing in the high privacy limit"
    - "f divergence inequalities"
    - "on maximal correlation mutual information and data privacy"
    - "euclidean information theory of networks"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "bounds on inference"

Connected through hyperedge HE-2 (induced by source paper "minimax filter learning to preserve privacy from inference attacks"):
  with members:
    - "learning privately from multiparty data"
    - "calibrating data to sensitivity in private data analysis"
    - "privacy against statistical inference"
    - "discriminately decreasing discriminability with learned image filters"
    - "differential privacy on the trade off between utility and information leakage"
    - "differential privacy via wavelet transforms"
    - "what can we learn privately"

Connected through hyperedge HE-3 (induced by source paper "privacy optimal strategies for smart metering systems with a rechargeable battery"):
  with members:
    - "optimal zero delay coding of markov sources stationary and finite memory codes"
    - "smart meter privacy with an energy harvesting device and instantaneous power constraints"
    - "beyond the entropy power inequality via rearrangements"
    - "increasing smart meter privacy through energy harvesting and storage devices"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "smart meter privacy a utility privacy framework"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-4 (induced by source paper "utility privacy tradeoffs in databases an information theoretic approach"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "privacy against statistical inference"
    - "on privacy preserving histograms"
    - "discriminatory lossy source coding side information privacy"
    - "competitive privacy in the smart grid an information theoretic approach"
    - "on the relation between differential privacy and quantitative information flow"
    - "secure source coding with a helper"
    - "privacy constraints in regularized convex optimization"

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)