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 "variable length compression allowing errors"):
  Abstract: This paper studies the fundamental limits of the minimum average length of lossless and lossy variable-length compression, allowing a nonzero error probability $\epsilon$, for lossless compression. We give non-asymptotic bounds on the minimum average length in terms of Erokhin's rate-distortion function and we use those bounds to obtain a Gaussian approximation on the speed of approach to the limit which is quite accurate for all but small blocklengths: $$(1 - \epsilon) k H(\mathsf S) - \sqrt{\frac{k V(\mathsf S)}{2 \pi} } e^{- \frac {(Q^{-1}(\epsilon))^2} 2 }$$ where $Q^{-1}(\cdot)$ is the functional inverse of the standard Gaussian complementary cdf, and $V(\mathsf S)$ is the source dispersion. A nonzero error probability thus not only reduces the asymptotically achievable rate by a factor of $1 - \epsilon$, but this asymptotic limit is approached from below, i.e. larger source dispersions and shorter blocklengths are beneficial. Variable-length lossy compression under an excess distortion constraint is shown to exhibit similar properties.

Includes members:
  Member "asymptotics and non asymptotics for universal fixed to variable source coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finite sample analysis of image registration")
  Member "variable length compression allowing errors extended":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the conditional smooth renyi entropy and its applications in guessing and source coding")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finite sample analysis of image registration")
  Member "the dispersion of lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse regression codes")

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)