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 "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.

Includes members:
  Member "polar codes for broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polar codes simplicity random codes durability")
  Member "finite length scaling for polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reed muller codes theory and algorithms")
  Member "polynomial complexity of polar codes for non binary alphabets key agreement and slepian wolf coding":
    also connected through other hyperedges:
      (none)
  Member "polar coding for interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "general strong polarization")
  Member "a new entropy power inequality for integer valued random variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an entropy power inequality for discrete random variables")
  Member "polar codes speed of polarization and polynomial gap to capacity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polar codes simplicity random codes durability")
  Member "polar codes for the two user multiple access channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar codes simplicity random codes durability")
  Member "achieving the secrecy capacity of wiretap channels using polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explicit codes for the wiretap channel a unified design framework")

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)