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 "multi target detection with application to cryo electron microscopy"):
  Abstract: We consider the multi-target detection problem of recovering a set of signals that appear multiple times at unknown locations in a noisy measurement. In the low noise regime, one can estimate the signals by first detecting occurrences, then clustering and averaging them. In the high noise regime however, neither detection nor clustering can be performed reliably, so that strategies along these lines are destined to fail. Notwithstanding, using autocorrelation analysis, we show that the impossibility to detect and cluster signal occurrences in the presence of high noise does not necessarily preclude signal estimation. Specifically, to estimate the signals, we derive simple relations between the autocorrelations of the observation and those of the signals. These autocorrelations can be estimated accurately at any noise level given a sufficiently long measurement. To recover the signals from the observed autocorrelations, we solve a set of polynomial equations through nonlinear least-squares. We provide analysis regarding well-posedness of the task, and demonstrate numerically the effectiveness of the method in a variety of settings. #R##N#The main goal of this work is to provide theoretical and numerical support for a recently proposed framework to image 3-D structures of biological macromolecules using cryo-electron microscopy in extreme noise levels.

Includes members:
  Member "multi target detection with an arbitrary spacing distribution":
    also connected through other hyperedges:
      (none)
  Member "toward single particle reconstruction without particle picking breaking the detection limit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities")
  Member "bispectrum inversion with application to multireference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities")
  Member "heterogeneous multireference alignment a single pass approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "harmonic alignment")
  Member "fourier phase retrieval uniqueness and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward a mathematical theory of the crystallographic phase retrieval problem")
  Member "the sample complexity of multi reference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blind identification of stochastic block models from dynamical observations")
  Member "multireference alignment is easier with an aperiodic translation distribution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "single particle cryo electron microscopy mathematical theory computational challenges and opportunities")
  Member "rapid robust and reliable blind deconvolution via nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")

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)