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: "structured sparsity discrete and convex approaches"
  Abstract: During the past decades, sparsity has been shown to be of significant importance in fields such as compression, signal sampling and analysis, machine learning, and optimization. In fact, most natural data can be sparsely represented, i.e., a small set of coefficients is sufficient to describe the data using an appropriate basis. Sparsity is also used to enhance interpretability in real-life applications, where the relevant information therein typically resides in a low dimensional space. However, the true underlying structure of many signal processing and machine learning problems is often more sophisticated than sparsity alone. In practice, what makes applications differ is the existence of sparsity patterns among coefficients. In order to better understand the impact of such structured sparsity patterns, in this chapter we review some realistic sparsity models and unify their convex and non-convex treatments. We start with the general group sparse model and then elaborate on two important special cases: the dispersive and hierarchical models. We also consider more general structures as defined by set functions and present their convex proxies. Further, we discuss efficient optimization solutions for structured sparsity problems and illustrate structured sparsity in action via three applications in image processing, neuronal signal processing, and confocal imaging.

Connected through hyperedge HE-1 (induced by source paper "optimal approximation for unconstrained non submodular minimization"):
  with members:
    - "near optimal approximate discrete and continuous submodular function minimization"
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"
    - "gradient methods for submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"

Connected through hyperedge HE-2 (induced by source paper "on the global local dichotomy in sparsity modeling"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "local sparsity and recovery of fusion frames structured signals"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "approximation vector machines for large scale online learning"
    - "boosting of image denoising algorithms"
    - "sparse modeling for image and vision processing"
    - "uniform recovery of fusion frame structured sparse signals"
    - "shift invariance sparse coding for audio classification"

Connected through hyperedge HE-3 (induced by source paper "quantitative recovery conditions for tree based compressed sensing"):
  with members:
    - "approximation algorithms for model based compressive sensing"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "learning with submodular functions a convex optimization perspective"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"
    - "phase transitions for greedy sparse approximation algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "convex block sparse linear regression with expanders provably"):
  with members:
    - "group sparse model selection hardness and relaxations"
    - "on model based rip 1 matrices"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"
    - "model based compressive sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)