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 "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets"):
  Abstract: Quantised random embeddings are an efficient dimensionality reduction technique which preserves the distances of low-complexity signals up to some controllable additive and multiplicative distortions. In this work, we instead focus on verifying when this technique preserves the separability of two disjoint closed convex sets, i.e., in a quantised view of the "rare eclipse problem" introduced by Bandeira et al. in 2014. This separability would ensure exact classification of signals in such sets from the signatures output by this non-linear dimensionality reduction. We here present a result relating the embedding's dimension, its quantiser resolution and the sets' separation, as well as some numerically testable conditions to illustrate it. Experimental evidence is then provided in the special case of two $\ell_2$-balls, tracing the phase transition curves that ensure these sets' separability in the embedded domain.

Includes members:
  Member "compressive classification and the rare eclipse problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing")
  Member "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "bounds on the number of measurements for reliable compressive classification":
    also connected through other hyperedges:
      (none)
  Member "time for dithering fast and quantized random embeddings via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "representation and coding of signal geometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "small width low distortions quantized random embeddings of low complexity sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "near optimal bounds for binary embeddings of arbitrary sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "one bit sensing of low rank and bisparse matrices")
  Member "quantization and compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")

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)