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: "learning exponential families in high dimensions strong convexity and sparsity"
  Abstract: The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions, such as when there is some sparsity pattern of the optimal parameter. This work characterizes a certain strong convexity property of general exponential families, which allow their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L_1 regularization.

Connected through hyperedge HE-1 (induced by source paper "regularized loss minimizers with local data perturbation consistency and data irrecoverability"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "learning structural changes of gaussian graphical models in controlled experiments"
    - "calibrating data to sensitivity in private data analysis"
    - "estimation of simultaneously sparse and low rank matrices"
    - "network flow algorithms for structured sparsity"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "reconstruction error bounds for compressed sensing under poisson or poisson gaussian noise using variance stabilization transforms"):
  with members:
    - "poisson matrix recovery and completion"
    - "the generalized lasso with non linear observations"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-3 (induced by source paper "reconstruction error bounds for compressed sensing under poisson noise using the square root of the jensen shannon divergence"):
  with members:
    - "poisson matrix recovery and completion"
    - "the generalized lasso with non linear observations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "performance bounds for expander based compressed sensing in poisson noise"
    - "stable restoration and separation of approximately sparse signals"
    - "sparse recovery by non convex optimization instance optimality"

Connected through hyperedge HE-4 (induced by source paper "exponential decay of reconstruction error from binary measurements of sparse signals"):
  with members:
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "two part reconstruction in compressed sensing"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "message passing de quantization with applications to compressed sensing"
    - "one bit compressed sensing with non gaussian measurements"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

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.LG(Machine Learning)