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: "polar coding for non stationary channels"
  Abstract: The problem of polar coding for an arbitrary sequence of independent binary-input memoryless symmetric (BMS) channels $\left\{W_i\right\}_{i=1}^{N}$ is considered. Such a sequence of channels is referred to as a non-stationary sequence of channels and arises in applications where data symbols experience different and independent channel characteristics. The sequence of channels is assumed to be completely known to both the transmitter and the receiver (a coherent scenario). Also, at each code block transmission, each of the channels is used only once. In other words, a codeword of length $N$ is constructed and then the $i$-th encoded bit is transmitted over $W_i$. The goal is to operate at a rate $R$ close to the average symmetric capacities of $W_i$'s, denoted by $\overline{I}_N$. To this end, we construct a polar coding scheme using Ar{\i}kan's channel polarization transform in combination with certain permutations at each polarization level and certain skipped operations. In particular, given a non-stationary sequence of BMS channels $\left\{W_i\right\}_{i=1}^{N}$ and $P_e$, where $0 < P_e <1$, we construct a polar code of length $N$ and rate $R$ guaranteeing a block error probability of at most $P_e$ for transmission over $\left\{W_i\right\}_{i=1}^{N}$ such that $$ N \leq \frac{\kappa}{(\overline{I}_N- R)^{\mu}}, $$ where $\mu$ is a constant and $\kappa$ is a constant depending on $P_e$ and $\mu$. We further show a numerical upper bound on $\mu$ that is: $\mu \leq 10.78$. The encoding and decoding complexities of the constructed polar code preserve $O(N \log N)$ complexity of Ar{\i}kan's polar codes.

Connected through hyperedge HE-1 (induced by source paper "secure polar coding for adversarial wiretap channel"):
  with members:
    - "achieving secrecy capacity of the wiretap channel and broadcast channel with a confidential component"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "polar coding for the general wiretap channel"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-2 (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"
    - "entropy bounds on abelian groups and the ruzsa divergence"

Connected through hyperedge HE-3 (induced by source paper "explicit polar codes with small scaling exponent"):
  with members:
    - "efficient decoding of polar codes with some 16 16 kernels"
    - "binary linear codes with optimal scaling polar codes with large kernels"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "fast polarization and finite length scaling for non stationary channels"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

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)