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 "sparse approximation list decoding and uncertainty principles"):
  Abstract: We consider list versions of sparse approximation problems, where unlike the existing results in sparse approximation that consider situations with unique solutions, we are interested in multiple solutions. We introduce these problems and present the first combinatorial results on the output list size. These generalize and enhance some of the existing results on threshold phenomenon and uncertainty principles in sparse approximations. Our definitions and results are inspired by similar results in list decoding. We also present lower bound examples that bolster our results and show they are of the appropriate size.

Includes members:
  Member "on sparse representation in fourier and local bases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tree based dictionary learning framework")
  Member "every list decodable code for high noise has abundant near optimal rate puncturings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on error decoding of locally repairable and partial mds codes")
  Member "on the list decodability of random linear codes with large error rates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized list decoding")
  Member "combinatorial limitations of average radius list decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized list decoding")
  Member "the sparsity gap uncertainty principles proportional to dimension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrete uncertainty principles and sparse signal processing")

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.IT(Information Theory)