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: "an entropy sumset inequality and polynomially fast convergence to shannon capacity over all alphabets"
  Abstract: We prove a lower estimate on the increase in entropy when two copies of a conditional random variable $X | Y$, with $X$ supported on $\mathbb{Z}_q=\{0,1,\dots,q-1\}$ for prime $q$, are summed modulo $q$. Specifically, given two i.i.d copies $(X_1,Y_1)$ and $(X_2,Y_2)$ of a pair of random variables $(X,Y)$, with $X$ taking values in $\mathbb{Z}_q$, we show \[ H(X_1 + X_2 \mid Y_1, Y_2) - H(X|Y) \ge \alpha(q) \cdot H(X|Y) (1-H(X|Y)) \] for some $\alpha(q) > 0$, where $H(\cdot)$ is the normalized (by factor $\log_2 q$) entropy. Our motivation is an effective analysis of the finite-length behavior of polar codes, and the assumption of $q$ being prime is necessary. For $X$ supported on infinite groups without a finite subgroup and no conditioning, a sumset inequality for the absolute increase in (unnormalized) entropy was shown by Tao (2010). #R##N#We use our sumset inequality to analyze Ar{\i}kan's construction of polar codes and prove that for any $q$-ary source $X$, where $q$ is any fixed prime, and any $\epsilon > 0$, polar codes allow {\em efficient} data compression of $N$ i.i.d. copies of $X$ into $(H(X)+\epsilon)N$ $q$-ary symbols, as soon as $N$ is polynomially large in $1/\epsilon$. We can get capacity-achieving source codes with similar guarantees for composite alphabets, by factoring $q$ into primes and combining different polar codes for each prime in factorization. #R##N#A consequence of our result for noisy channel coding is that for {\em all} discrete memoryless channels, there are explicit codes enabling reliable communication within $\epsilon > 0$ of the symmetric Shannon capacity for a block length and decoding complexity bounded by a polynomial in $1/\epsilon$. The result was previously shown for the special case of binary input channels (Guruswami-Xia '13 and Hassani-Alishahi-Urbanke '13), and this work extends the result to channels over any alphabet.

Connected through hyperedge HE-1 (induced by source paper "countably infinite multilevel source polarization for non stationary erasure distributions"):
  with members:
    - "asymptotic distribution of multilevel channel polarization for a certain class of erasure channels"
    - "on the polarization levels of automorphic symmetric channels"
    - "general strong polarization"
    - "fano type inequalities based on general conditional information measures over countably infinite alphabets with list decoding"
    - "modular arithmetic erasure channels and their multilevel channel polarization"
    - "fourier analysis of mac polarization"
    - "a generalized erasure channel in the sense of polarization for binary erasure channels"
    - "polar coding for non stationary channels"

Connected through hyperedge HE-2 (induced by source paper "general strong polarization"):
  with members:
    - "on error correction performance and implementation of polar code list decoders for 5g"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "polar codes for broadcast channels"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "polar coding for interference networks"
    - "polar codes speed of polarization and polynomial gap to capacity"

Connected through hyperedge HE-3 (induced by source paper "synchronization strings codes for insertions and deletions approaching the singleton bound"):
  with members:
    - "synchronization strings channel simulations and interactive coding for insertions and deletions"
    - "synchronization strings explicit constructions local decoding and applications"
    - "efficiently decodable insertion deletion codes for high noise and high rate regimes"
    - "coding for interactive communication correcting insertions and deletions"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "interactive channel capacity revisited"
    - "deletion codes in the high noise and high rate regimes"
    - "polar codes speed of polarization and polynomial gap to capacity"

Connected through hyperedge HE-4 (induced by source paper "near optimal finite length scaling for polar codes over large alphabets"):
  with members:
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "on the finite length scaling of ternary polar codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "binary polarization kernels from code decompositions"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "improved bounds on the finite length scaling of polar codes"
    - "rate dependent analysis of the asymptotic behavior of channel polarization"

Connected through hyperedge HE-5 (induced by source paper "construction of polar codes with sublinear complexity"):
  with members:
    - "strange attractor for efficient polar code design"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "algebraic properties of polar codes from a new polynomial formalism"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "improved bounds on the finite length scaling of polar codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-6 (induced by source paper "on the construction of polar codes for channels with moderate input alphabet sizes"):
  with members:
    - "construction of polar codes for arbitrary discrete memoryless channels"
    - "upgraded approximation of non binary alphabets for polar code construction"
    - "channel upgradation for non binary input alphabets and macs"
    - "on the construction of polar codes"
    - "source and channel polarization over finite fields and reed solomon matrices"
    - "how to construct polar codes"
    - "polar codes with mixed kernels"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-7 (induced by source paper "on the finite length scaling of ternary polar codes"):
  with members:
    - "finite length scaling for polar codes"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "improved bounds on the finite length scaling of polar codes"
    - "an entropy inequality for q ary random variables and its application to channel polarization"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "polarization for arbitrary discrete memoryless channels"
    - "on the rate of channel polarization"

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)