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 "optimal resolution of change point detection with empirically observed statistics and erasures"):
  Abstract: This paper revisits the offline change-point detection problem from a statistical learning perspective. Instead of assuming that the underlying pre- and post-change distributions are known, it is assumed that we have partial knowledge of these distributions based on empirically observed statistics in the form of training sequences. Our problem formulation finds a variety of real-life applications from detecting when climate change occurred to detecting when a virus mutated. Using the training sequences as well as the test sequence consisting of a single-change and allowing for the erasure or rejection option, we derive the optimal resolution between the estimated and true change-points under two different asymptotic regimes on the undetected error probability---namely, the large and moderate deviations regimes. In both regimes, strong converses are also proved. In the moderate deviations case, the optimal resolution is a simple function of a symmetrized version of the chi-square distance.

Includes members:
  Member "distributed detection with empirically observed statistics":
    also connected through other hyperedges:
      (none)
  Member "second order asymptotically optimal statistical classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed detection with empirically observed statistics")
  Member "selective review of offline change point detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised non parametric change point detection in quasi periodic signals")
  Member "time division is optimal for covert communication over some broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "undetectable radios covert communication under spectral mask constraints")
  Member "fundamental limits of communication with low probability of detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "keyless covert communication via channel state information")
  Member "covert communication over noisy channels a resolvability perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keyless covert communication via channel state information")
  Member "asymptotic estimates in information theory with non vanishing error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finite sample analysis of image registration")
  Member "asymptotically optimal matching of multiple sequences to source distributions and training sequences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "asymptotic limits of privacy in bayesian time series matching")

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)