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 "fundamental limits of phasemax for phase retrieval a replica analysis"):
  Abstract: We consider a recently proposed convex formulation, known as the PhaseMax method, for solving the phase retrieval problem. Using the replica method from statistical mechanics, we analyze the performance of PhaseMax in the high-dimensional limit. Our analysis predicts the \emph{exact} asymptotic performance of PhaseMax. In particular, we show that a sharp phase transition phenomenon takes place, with a simple analytical formula characterizing the phase transition boundary. This result shows that the oversampling ratio required by existing performance bounds in the literature can be significantly reduced. Numerical results confirm the validity of our replica analysis, showing that the theoretical predictions are in excellent agreement with the actual performance of the algorithm, even for moderate signal dimensions.

Includes members:
  Member "phase transitions of spectral initialization for high dimensional nonconvex estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "corruption robust phase retrieval via linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solving equations of random convex functions via anchored regression")
  Member "phasemax convex phase retrieval via basis pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "phase retrieval meets statistical learning theory a flexible convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "phase retrieval via wirtinger flow theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "a field guide to forward backward splitting with a fasta implementation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards compact convnets via structure sparsity regularized filter pruning")
  Member "sparse phase retrieval convex algorithms and limitations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the capacity of waveform channels under square law detection of time limited signals")
  Member "asymptotic analysis of map estimation via the replica method and applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")

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)