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 "phase retrieval without small ball probability assumptions"):
  Abstract: In the context of the phase retrieval problem, it is known that certain natural classes of measurements, such as Fourier measurements and random Bernoulli measurements, do not lead to the unique reconstruction of all possible signals, even in combination with certain practically feasible random masks. To avoid this difficulty, the analysis is often restricted to measurement ensembles (or masks) that satisfy a small-ball probability condition, in order to ensure that the reconstruction is unique. This paper shows a complementary result: for random Bernoulli measurements, there is still a large class of signals that  can  be reconstructed uniquely, namely, those signals that are  non-peaky . In fact, this result is much more general: it holds for random measurements sampled from  any  subgaussian distribution    $ \mathcal {D}$   , without any small-ball conditions. This is demonstrated in two ways: 1) a proof of  stability and uniqueness  and 2) a uniform recovery guarantee for the  PhaseLift algorithm . In all of these cases, the number of measurements    $m$    approaches the information-theoretic lower bound. Finally, for random Bernoulli measurements with erasures, it is shown that PhaseLift achieves uniform recovery of all signals (including peaky ones).

Includes members:
  Member "improved recovery guarantees for phase retrieval from coded diffraction patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complex phase retrieval from subgaussian measurements")
  Member "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "a partial derandomization of phaselift using spherical designs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complex phase retrieval from subgaussian measurements")
  Member "phase retrieval with application to optical imaging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization")
  Member "low rank matrix recovery from rank one measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "phase retrieval from coded diffraction patterns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "phase retrieval stability and recovery guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "consensus based optimization on the sphere ii convergence to global minimizers and machine learning")
  Member "one bit compressed sensing with non gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressive spectrum sensing for cognitive radio networks")

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)