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 "estimation in the group action channel"):
  Abstract: We analyze the problem of estimating a signal from multiple measurements on a $\mbox{group action channel}$ that linearly transforms a signal by a random group action followed by a fixed projection and additive Gaussian noise. This channel is motivated by applications such as multi-reference alignment and cryo-electron microscopy. We focus on the large noise regime prevalent in these applications. We give a lower bound on the mean square error (MSE) of any asymptotically unbiased estimator of the signal's orbit in terms of the signal's moment tensors, which implies that the MSE is bounded away from 0 when $N/\sigma^{2d}$ is bounded from above, where $N$ is the number of observations, $\sigma$ is the noise standard deviation, and $d$ is the so-called $\mbox{moment order cutoff}$. In contrast, the maximum likelihood estimator is shown to be consistent if $N /\sigma^{2d}$ diverges.

Includes members:
  Member "bispectrum inversion with application to multireference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "multireference alignment is easier with an aperiodic translation distribution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "sample complexity of the boolean multireference alignment problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral methods from tensor networks")
  Member "a certifiably correct algorithm for synchronization over the special euclidean group":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certifiably globally optimal extrinsic calibration from per sensor egomotion")
  Member "f divergence inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on implicit regularization in beta vaes")

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)