human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a submodularity based agglomerative clustering algorithm for the privacy funnel"):
  Abstract: For the privacy funnel (PF) problem, we propose an efficient iterative agglomerative clustering algorithm based on the minimization of the difference of submodular functions (IAC-MDSF). For a data curator that wants to share the data $X$ correlated with the sensitive information $S$, the PF problem is to generate the sanitized data $\hat{X}$ that maintains a specified utility/fidelity threshold on $I(X; \hat{X})$ while minimizing the privacy leakage $I(S; \hat{X})$. Our IAC-MDSF algorithm starts with the original alphabet $\hat{\mathcal{X}} := \mathcal{X}$ and iteratively merges the elements in the current alphabet $\hat{\mathcal{X}}$ that minimizes the Lagrangian function $ I(S;\hat{X}) - \lambda I(X;\hat{X}) $. We prove that the best merge in each iteration of IAC-MDSF can be searched efficiently over all subsets of $\hat{\mathcal{X}}$ by the existing MDSF algorithms. We show that the IAC-MDSF algorithm also applies to the information bottleneck (IB), a dual problem to PF. By varying the value of the Lagrangian multiplier $\lambda$, we obtain the experimental results on a heart disease data set in terms of the Pareto frontier: $ I(S;\hat{X})$ vs. $- I(X;\hat{X})$. We show that our IAC-MDSF algorithm outperforms the existing iterative pairwise merge approaches for both PF and IB and is computationally much less complex.

Includes members:
  Member "generalizing bottleneck problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "obfuscation via information density estimation")
  Member "from the information bottleneck to the privacy funnel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "algorithms for approximate minimization of the difference between submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "privacy against statistical inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "a submodular supermodular procedure with applications to discriminative structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a unified framework of robust submodular optimization")
  Member "convex analysis and optimization with submodular functions a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polynomial time algorithms for submodular laplacian systems")

Question: Please tell me which class the source paper 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)