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 "ofdm channel estimation via phase retrieval"):
  Abstract: Pilot-aided channel estimation is nowadays a standard component in each wireless receiver enabling coherent transmission of complex-valued constellations, only affected by noise and interference. Whenever these disturbances are sufficiently small and long data frames are used, high data rates can be achieved and the resource overhead due to the pilots vanishes asymptotically. On the other, it is expected that for the next generation of mobile networks not only data rate is in the main focus but also low latency, short and sporadic messages, massive connectivity, distributed & adhoc processing and robustness with respect to asynchronism. Therefore a review of several well-established principles in communication has been started already. A particular implication when using complex-valued pilots is that these values have to be known at the receiver and therefore these resources can not be used simultaneously for user data. For an OFDM-like multicarrier scheme this means that pilot tones (usually placed equidistantly according to the Nyquist theorem) are allocated with globally known amplitudes and phases to reconstruct the channel impulse response. Phases are designed and allocated globally which is in contrast to a distributed infrastructure. In this work we present therefore a new phaseless pilot scheme where only pilot amplitudes need to be known at the receiver, i.e., phases are available again and can be used for various other purposes. The idea is based on a phase retrieval result for symmetrized and zero-padded magnitude Fourier measurements obtained by two of the authors. The phases on the pilot tones can now be used to carry additional user-specific data or compensate for other signal characteristics, like the PAPR.

Includes members:
  Member "stft phase retrieval uniqueness guarantees and recovery algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "phase retrieval an overview of recent developments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "on the stability of sparse convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stable blind deconvolution over the reals from additional autocorrelations")
  Member "stable recovery from the magnitude of symmetrized fourier measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blind deconvolution with additional autocorrelations via convex programs")
  Member "phase retrieval for sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stable phaseless sampling and reconstruction of real valued signals with finite rate of innovations")
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  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 "phaselift exact and stable signal recovery from magnitude measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")

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)