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 "capacity approaching phasecode for low complexity compressive phase retrieval"):
  Abstract: In this paper, we tackle the general compressive phase retrieval problem. The problem is to recover a K-sparse complex vector of length n, $x\in \mathbb{C}^n$, from the magnitudes of m linear measurements, $y=|Ax|$, where $A \in \mathbb{C}^{m \times n}$ can be designed, and the magnitudes are taken component-wise for vector $Ax\in \mathbb{C}^m$. We propose a variant of the PhaseCode algorithm, and show that, using an irregular left-degree sparse-graph code construction, the algorithm can recover almost all the K non-zero signal components using only slightly more than 4K measurements under some mild assumptions, with optimal time and memory complexity of ${\cal O}(K)$. It is known that the fundamental limit for the number of measurements in compressive phase retrieval problem is $4K - o(K)$. To the best of our knowledge, this is the first constructive capacity-approaching compressive phase retrieval algorithm. As a second contribution, we propose another variant of the PhaseCode algorithm that is based on a Compressive Sensing framework involving sparse-graph codes. Our proposed algorithm is an instance of a more powerful "separation" architecture that can be used to address the compressive phase-retrieval problem in general. This modular design features a compressive sensing outer layer, and a trigonometric-based phase-retrieval inner layer. The compressive-sensing layer operates as a linear phase-aware compressive measurement subsystem, while the trig-based phase-retrieval layer provides the desired abstraction between the actually targeted nonlinear phase-retrieval problem and the induced linear compressive-sensing problem. Invoking this architecture based on the use of sparse-graph codes for the compressive sensing layer, we show that we can exactly recover a signal from only the magnitudes of its linear measurements using only slightly more than 6K measurements.

Includes members:
  Member "fast and robust compressive phase retrieval with sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "compressive phase retrieval via generalized approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "phasecode fast and efficient compressive phase retrieval based on sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "fast compressive phase retrieval from fourier measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on fast decoding of high dimensional signals from one bit measurements")
  Member "sub linear time support recovery for compressed sensing using sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse graph codes for non adaptive quantitative group testing")
  Member "reliable deniable and hidable communication over multipath networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stealthy communication over adversarially jammed multipath networks")
  Member "super sparse signals with unknown phases efficiently recovered":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "new conditions for sparse phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "almost optimal phaseless compressed sensing with sublinear decoding time")

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)