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: "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
  Abstract: Consider the transmission of a polar code of block length $N$ and rate $R$ over a binary memoryless symmetric channel $W$ and let $P_e$ be the block error probability under successive cancellation decoding. In this paper, we develop new bounds that characterize the relationship of the parameters $R$, $N$, $P_e$, and the quality of the channel $W$ quantified by its capacity $I(W)$ and its Bhattacharyya parameter $Z(W)$. #R##N#In previous work, two main regimes were studied. In the error exponent regime, the channel $W$ and the rate $R<I(W)$ are fixed, and it was proved that the error probability $P_e$ scales roughly as $2^{-\sqrt{N}}$. In the scaling exponent approach, the channel $W$ and the error probability $P_e$ are fixed and it was proved that the gap to capacity $I(W)-R$ scales as $N^{-1/\mu}$. Here, $\mu$ is called scaling exponent and this scaling exponent depends on the channel $W$. A heuristic computation for the binary erasure channel (BEC) gives $\mu=3.627$ and it was shown that, for any channel $W$, $3.579 \le \mu \le 5.702$. #R##N#Our contributions are as follows. First, we provide the tighter upper bound $\mu \le 4.714$ valid for any $W$. With the same technique, we obtain $\mu \le 3.639$ for the case of the BEC, which approaches very closely its heuristically derived value. Second, we develop a trade-off between the gap to capacity $I(W)-R$ and the error probability $P_e$ as functions of the block length $N$. In other words, we consider a moderate deviations regime in which we study how fast both quantities, as functions of the block length $N$, simultaneously go to $0$. Third, we prove that polar codes are not affected by error floors. To do so, we fix a polar code of block length $N$ and rate $R$. Then, we vary the channel $W$ and we show that the error probability $P_e$ scales as the Bhattacharyya parameter $Z(W)$ raised to a power that scales roughly like $\sqrt{N}$.

Connected through hyperedge HE-1 (induced by source paper "on the polarizing behavior and scaling exponent of polar codes with product kernels"):
  with members:
    - "explicit polar codes with small scaling exponent"
    - "minimum distance based construction of multi kernel polar codes"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "finite length scaling for polar codes"
    - "list decoding of polar codes"
    - "polar codes characterization of exponent bounds and constructions"

Connected through hyperedge HE-2 (induced by source paper "polar codes simplicity random codes durability"):
  with members:
    - "reed muller codes polarize"
    - "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"
    - "explicit polar codes with small scaling exponent"
    - "on the capacity of channels with deletions and states"
    - "polar codes for the deletion channel weak and strong polarization"
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"

Connected through hyperedge HE-3 (induced by source paper "sublinear latency for simplified successive cancellation decoding of polar codes"):
  with members:
    - "log logarithmic time pruned polar coding"
    - "polar codes with exponentially small error at finite block length"
    - "polar code moderate deviation recovering the scaling exponent"
    - "partitioned list decoding of polar codes analysis and improvement of finite length performance"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "fast and flexible successive cancellation list decoders for polar codes"
    - "binary linear codes with optimal scaling polar codes with large kernels"
    - "partitioned successive cancellation list decoding of polar codes"

Connected through hyperedge HE-4 (induced by source paper "log logarithmic time pruned polar coding"):
  with members:
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "polar codes with exponentially small error at finite block length"
    - "general strong polarization"
    - "polar code moderate deviation recovering the scaling exponent"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"
    - "flexible length polar codes through graph based augmentation"
    - "a packing lemma for polar codes"

Connected through hyperedge HE-5 (induced by source paper "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"):
  with members:
    - "explicit polar codes with small scaling exponent"
    - "general strong polarization"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "multi kernel construction of polar codes"
    - "finite length scaling for polar codes"
    - "polar codes speed of polarization and polynomial gap to capacity"

Connected through hyperedge HE-6 (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"
    - "polar coding for non stationary channels"
    - "fast polarization and finite length scaling for non stationary channels"
    - "finite length scaling for polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-7 (induced by source paper "the penalty in scaling exponent for polar codes is analytically approximated by the golden ratio"):
  with members:
    - "explicit polar codes with small scaling exponent"
    - "finite length scaling for polar codes"

Connected through hyperedge HE-8 (induced by source paper "channel coding at low capacity"):
  with members:
    - "polar codes with a stepped boundary"
    - "recursive list decoding for reed muller codes"
    - "beta beta bounds finite blocklength analog of the golden formula"
    - "relaxed polar codes"
    - "achieving the uniform rate region of general multiple access channels by polar coding"
    - "coding in the finite blocklength regime bounds based on laplace integrals and their asymptotic approximations"
    - "polar codes for broadcast channels"
    - "asymptotic estimates in information theory with non vanishing error probabilities"

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)