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 "recovery of structured signals from corrupted non linear measurements"):
  Abstract: This paper studies the problem of recovering a structured signal from a relatively small number of corrupted non-linear measurements. Assuming that signal and corruption are contained in some structure-promoted set, we suggest an extended Lasso to disentangle signal and corruption. We also provide conditions under which this recovery procedure can successfully reconstruct both signal and corruption.

Includes members:
  Member "corrupted sensing with sub gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stable recovery of structured signals from corrupted sub gaussian measurements")
  Member "stable recovery of structured signals from corrupted sub gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recovery of structured signals with prior information via maximizing correlation")
  Member "on the phase transition of corrupted sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stable recovery of structured signals from corrupted sub gaussian measurements")
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "the generalized lasso with non linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "corrupted sensing novel guarantees for separating structured signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "sharp recovery bounds for convex demixing with applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")

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)