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: "hypothesis testing in the high privacy limit"
  Abstract: Binary hypothesis testing under the Neyman-Pearson formalism is a statistical inference framework for distinguishing data generated by two different source distributions. Privacy restrictions may require the curator of the data or the data respondents themselves to share data with the test only after applying a randomizing privacy mechanism. Using mutual information as the privacy metric and the relative entropy between the two distributions of the output (postrandomization) source classes as the utility metric (motivated by the Chernoff-Stein Lemma), this work focuses on finding an optimal mechanism that maximizes the chosen utility function while ensuring that the mutual information based leakage for both source distributions is bounded. Focusing on the high privacy regime, an Euclidean information-theoretic (E-IT) approximation to the tradeoff problem is presented. It is shown that the solution to the E-IT approximation is independent of the alphabet size and clarifies that a mutual information based privacy metric preserves the privacy of the source symbols in inverse proportion to their likelihood.

Connected through hyperedge HE-1 (induced by source paper "privacy with estimation guarantees"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "generalizing bottleneck problems"
    - "matching anonymized and obfuscated time series to users profiles"
    - "privacy preserving adversarial networks"
    - "renyi differential privacy"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "an estimation theoretic view of privacy"

Connected through hyperedge HE-2 (induced by source paper "privacy against a hypothesis testing adversary"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "synthesizing robust adversarial examples"
    - "adversarial machine learning at scale"
    - "source distinguishability under distortion limited attack an optimal transport perspective"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"
    - "design of binary quantizers for distributed detection under secrecy constraints"
    - "on the relation between identifiability differential privacy and mutual information privacy"

Connected through hyperedge HE-3 (induced by source paper "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning"):
  with members:
    - "iterative orthogonal feature projection for diagnosing bias in black box models"
    - "discrimination in online ad delivery"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "evaluation of marton s inner bound for the general broadcast channel"
    - "evaluation of marton s inner bound for the general broadcast channel"

Connected through hyperedge HE-4 (induced by source paper "an estimation theoretic view of privacy"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "generalizing bottleneck problems"
    - "matching anonymized and obfuscated time series to users profiles"
    - "privacy preserving adversarial networks"
    - "context aware generative adversarial privacy"
    - "principal inertia components and applications"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-5 (induced by source paper "matching anonymized and obfuscated time series to users profiles"):
  with members:
    - "robust privacy utility tradeoffs under differential privacy and hamming distortion"
    - "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"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"

Connected through hyperedge HE-6 (induced by source paper "hypothesis testing under mutual information privacy constraints in the high privacy regime"):
  with members:
    - "privacy aware mmse estimation"
    - "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"
    - "the awgn red alert problem"

Connected through hyperedge HE-7 (induced by source paper "hypothesis testing under maximal leakage privacy constraints"):
  with members:
    - "euclidean information theory of networks"

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)