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: "group sparse model selection hardness and relaxations"
  Abstract: Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these models is the recovery of "interpretable" signals through the identification of their constituent groups. In this paper, we establish a combinatorial framework for group-model selection problems and highlight the underlying tractability issues. In particular, we show that the group-model selection problem is equivalent to the well-known NP-hard weighted maximum coverage problem (WMC). Leveraging a graph-based understanding of group models, we describe group structures which enable correct model selection in polynomial time via dynamic programming. Furthermore, group structures that lead to totally unimodular constraints have tractable discrete as well as convex relaxations. We also present a generalization of the group-model that allows for within group sparsity, which can be used to model hierarchical sparsity. Finally, we study the Pareto frontier of group-sparse approximations for two tractable models, among which the tree sparsity model, and illustrate selection and computation trade-offs between our framework and the existing convex relaxations.

Connected through hyperedge HE-1 (induced by source paper "an adaptive sublinear time block sparse fourier transform"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "on model based rip 1 matrices"
    - "nearly optimal sparse fourier transform"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "learning based compressive subsampling"):
  with members:
    - "a totally unimodular view of structured sparsity"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "energy aware adaptive bi lipschitz embeddings"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "on variable density compressive sampling"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "convex block sparse linear regression with expanders provably"):
  with members:
    - "structured sparsity discrete and convex approaches"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "structured sparsity discrete and convex approaches"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a novel sparsity and clustering regularization"
    - "on model based rip 1 matrices"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "phase retrieval via matrix completion"
    - "convex approaches to model wavelet sparsity patterns"

Connected through hyperedge HE-5 (induced by source paper "uniform recovery of fusion frame structured sparse signals"):
  with members:
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "recovering low rank matrices from few coefficients in any basis"
    - "sparse recovery from combined fusion frame measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "block length dependent thresholds in block sparse compressed sensing"
    - "block sparsity coherence and efficient recovery"

Connected through hyperedge HE-6 (induced by source paper "a totally unimodular view of structured sparsity"):
  with members:
    - "provable submodular minimization using wolfe s algorithm"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "learning with submodular functions a convex optimization perspective"
    - "structured sparsity inducing norms through submodular functions"
    - "is submodularity testable"
    - "model based compressive sensing"

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)