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 "heterogeneous multireference alignment a single pass approach"):
  Abstract: Multireference alignment (MRA) is the problem of estimating a signal from many noisy and cyclically shifted copies of itself. In this paper, we consider an extension called heterogeneous MRA, where $K$ signals must be estimated, and each observation comes from one of those signals, unknown to us. This is a simplified model for the heterogeneity problem notably arising in cryo-electron microscopy. We propose an algorithm which estimates the $K$ signals without estimating either the shifts or the classes of the observations. It requires only one pass over the data and is based on low-order moments that are invariant under cyclic shifts. Given sufficiently many measurements, one can estimate these invariant features averaged over the $K$ signals. We then design a smooth, non-convex optimization problem to compute a set of signals which are consistent with the estimated averaged features. We find that, in many cases, the proposed approach estimates the set of signals accurately despite non-convexity, and conjecture the number of signals $K$ that can be resolved as a function of the signal length $L$ is $\sim \sqrt{L}$.

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 "painless breakups efficient demixing of low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blind demixing for low latency communication")
  Member "the sample complexity of multi reference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyper molecules on the representation and recovery of dynamical structures with application to flexible macro molecular structures in cryo em")
  Member "a max cut approach to heterogeneity in cryo electron microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "continuously heterogeneous hyper objects in cryo em and 3 d movies of many temporal dimensions")
  Member "a representation theory perspective on simultaneous alignment and classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyper molecules on the representation and recovery of dynamical structures with application to flexible macro molecular structures in cryo em")
  Member "fundamental limits in multi image alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finite sample analysis of image registration")
  Member "heterogeneous tensor decomposition for clustering via manifold optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vectorial dimension reduction for tensors based on bayesian inference")

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)