human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "variable length compression allowing errors extended"
  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: $$(1 - \epsilon) k H(\mathsf S) - \sqrt{\frac{k V(\mathsf S)}{2 \pi} } e^{- \frac { \left(Q^{-1}(\epsilon)\right)^2} 2 }$$ where $Q^{-1}(\cdot)$ is the functional inverse of the $Q$-function 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. a larger source dispersion and shorter blocklengths are beneficial. Variable-length lossy compression under excess distortion constraint is shown to exhibit similar properties.

Connected through hyperedge HE-1 (induced by source paper "on the conditional smooth renyi entropy and its applications in guessing and source coding"):
  with members:
    - "non asymptotic fundamental limits of guessing subject to distortion"
    - "a characterization of guesswork on swiftly tilting curves"
    - "non asymptotic and asymptotic fundamental limits of guessing subject to distortion"
    - "multi user guesswork and brute force security"
    - "on the smooth renyi entropy and variable length source coding allowing errors"
    - "variable length compression allowing errors"
    - "guesswork large deviations and shannon entropy"
    - "guessing revisited a large deviations approach"

Connected through hyperedge HE-2 (induced by source paper "strong converses for group testing in the finite blocklength regime"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing with prior statistics"
    - "bitwise map algorithm for group testing based on holographic transformation"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "variable length compression allowing errors"
    - "the capacity of adaptive group testing"
    - "adaptive group testing as channel coding with feedback"

Connected through hyperedge HE-3 (induced by source paper "joint source channel coding with feedback"):
  with members:
    - "joint source channel coding with feedback extended"
    - "minimum energy to send k bits over multiple antenna fading channels"
    - "asymptotic expansions for gaussian channels with feedback under a peak power constraint"
    - "variable length compression allowing errors"
    - "active sequential hypothesis testing"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"
    - "information theoretic viewpoints on optimal causal coding decoding problems"

Connected through hyperedge HE-4 (induced by source paper "on the smooth renyi entropy and variable length source coding allowing errors"):
  with members:
    - "variable length compression allowing errors"
    - "on optimum strategies for minimizing the exponential moments of a given cost function"
    - "simple channel coding bounds"

Connected through hyperedge HE-5 (induced by source paper "an information spectrum approach to weak variable length source coding with side information"):
  with members:
    - "on the reliability function of variable rate slepian wolf coding"
    - "on the dispersions of three network information theory problems"
    - "variable length compression allowing errors"
    - "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"
    - "universal wyner ziv coding for distortion constrained general side information"
    - "second order slepian wolf coding theorems for non mixed and mixed sources"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-6 (induced by source paper "variable length compression allowing errors"):
  with members:
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"

Question: Please tell me which class the center node 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)