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: "generalizing bottleneck problems"
  Abstract: Given a pair of random variables $(X,Y)\sim P_{XY}$ and two convex functions $f_1$ and $f_2$, we introduce two bottleneck functionals as the lower and upper boundaries of the two-dimensional convex set that consists of the pairs $\left(I_{f_1}(W; X), I_{f_2}(W; Y)\right)$, where $I_f$ denotes $f$-information and $W$ varies over the set of all discrete random variables satisfying the Markov condition $W \to X \to Y$. Applying Witsenhausen and Wyner's approach, we provide an algorithm for computing boundaries of this set for $f_1$, $f_2$, and discrete $P_{XY}$, . In the binary symmetric case, we fully characterize the set when (i) $f_1(t)=f_2(t)=t\log t$, (ii) $f_1(t)=f_2(t)=t^2-1$, and (iii) $f_1$ and $f_2$ are both $\ell^\beta$ norm function for $\beta > 1$. We then argue that upper and lower boundaries in (i) correspond to Mrs. Gerber's Lemma and its inverse (which we call Mr. Gerber's Lemma), in (ii) correspond to estimation-theoretic variants of Information Bottleneck and Privacy Funnel, and in (iii) correspond to Arimoto Information Bottleneck and Privacy Funnel.

Connected through hyperedge HE-1 (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-2 (induced by source paper "estimation efficiency under privacy constraints"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "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"
    - "information extraction under privacy constraints"

Connected through hyperedge HE-3 (induced by source paper "a submodularity based agglomerative clustering algorithm for the privacy funnel"):
  with members:
    - "from the information bottleneck to the privacy funnel"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "privacy against statistical inference"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "convex analysis and optimization with submodular functions a tutorial"

Connected through hyperedge HE-4 (induced by source paper "on the robustness of information theoretic privacy measures and mechanisms"):
  with members:
    - "data disclosure under perfect sample privacy"
    - "privacy against brute force inference attacks"
    - "estimation efficiency under privacy constraints"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "the utility cost of robust privacy guarantees"
    - "privacy under hard distortion constraints"
    - "a tunable measure for information leakage"

Connected through hyperedge HE-5 (induced by source paper "privacy with estimation guarantees"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "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"
    - "privacy aware mmse estimation"

Connected through hyperedge HE-6 (induced by source paper "a correlation measure based on vector valued l_p norms"):
  with members:
    - "exact exponent for soft covering"
    - "exact soft covering exponent"
    - "measuring secrecy by the probability of a successful guess"
    - "r enyi resolvability and its applications to the wiretap channel"
    - "exact random coding secrecy exponents for the wiretap channel"
    - "a large deviations approach to secure lossy compression"
    - "f divergence inequalities"
    - "semantic security capacity for wiretap channels of type ii"

Connected through hyperedge HE-7 (induced by source paper "the variational deficiency bottleneck"):
  with members:
    - "unique informations and deficiencies"
    - "nonlinear information bottleneck"
    - "deep learning and the information bottleneck principle"
    - "quantifying unique information"

Connected through hyperedge HE-8 (induced by source paper "an estimation theoretic view of privacy"):
  with members:
    - "the utility cost of robust privacy guarantees"
    - "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"
    - "hypothesis testing in the high privacy limit"

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)