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: "a totally unimodular view of structured sparsity"
  Abstract: This paper describes a simple framework for structured sparse recovery based on convex optimization. We show that many structured sparsity models can be naturally represented by linear matrix inequalities on the support of the unknown parameters, where the constraint matrix has a totally unimodular (TU) structure. For such structured models, tight convex relaxations can be obtained in polynomial time via linear programming. Our modeling framework unifies the prevalent structured sparsity norms in the literature, introduces new interesting ones, and renders their tightness and tractability arguments transparent.

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 "combinatorial penalties which structures are preserved by convex relaxations"):
  with members:
    - "gradient methods for submodular maximization"
    - "near optimal nonmyopic value of information in graphical models"
    - "optimization with sparsity inducing penalties"
    - "learning with submodular functions a convex optimization perspective"
    - "structured sparsity inducing norms through submodular functions"
    - "model based compressive sensing"
    - "submodular functions extensions distributions and algorithms a survey"

Connected through hyperedge HE-3 (induced by source paper "on the projection operator to a three view cardinality constrained set"):
  with members:
    - "approximation algorithms for model based compressive sensing"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "structured sparsity through convex optimization"
    - "model based compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "learning based compressive subsampling"):
  with members:
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "group sparse model selection hardness and relaxations"
    - "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-5 (induced by source paper "learning data triage linear decoding works for compressive mri"):
  with members:
    - "learning with submodular functions a convex optimization perspective"
    - "model based compressive sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "group sparse model selection hardness and relaxations"):
  with members:
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "convex approaches to model wavelet sparsity patterns"
    - "total variation regularization for fmri based prediction of behavior"
    - "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)