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 "learning with forest sparsity"):
  Abstract: In this paper, we investigate a new model called \emph{forest sparsity} for sparse learning and compressive sensing. It is an extension of standard sparsity when the support set of the data is consisted of a series of mutually correlated trees. Forest sparsity exists in many practical applications such as multi-contrast MRI, parallel MRI, multispectral image and color image recovery. We theoretically prove the benefit of forest sparsity, that much less measurements are required for successful recovery in compressive sensing. Moreover, a new algorithm is proposed and applied on several applications with forest sparsity. All experimental results validate the superiority of forest sparsity.

Includes members:
  Member "efficient high dimensional inference in the multiple measurement vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "collaborative spectrum sensing from sparse observations in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "convex approaches to model wavelet sparsity patterns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hybrid approximate message passing")
  Member "compressive imaging using approximate message passing and a markov tree prior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity optimality of amp in coded systems")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "distributed compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "serial quantization for sparse time sequences")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "consistency of the group lasso and multiple kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint user selection and precoding in multiuser mimo systems via group lasso")

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.LG(Machine Learning)