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: "finite length scaling for polar codes"
  Abstract: Consider a binary-input memoryless output-symmetric channel W. Such a channel has a capacity, call it I(W), and for any R <; I(W) and strictly positive constant Pe we know that we can construct a coding scheme that allows transmission at rate R with an error probability not exceeding P#N# e#N#. Assume now that we let the rate R tend to I(W) and we ask how we have to scale the blocklength N in order to keep the error probability fixed to P#N# e#N#. We refer to this as the finite-length scaling behavior. This question was addressed by Strassen as well as Polyanskiy, Poor, and Verdu, and the result is that N must grow at least as the square of the reciprocal of I(W) - R. Polar codes are optimal in the sense that they achieve capacity. In this paper, we are asking to what degree they are also optimal in terms of their finite-length behavior. Since the exact scaling behavior depends on the choice of the channel, our objective is to provide scaling laws that hold universally for all binary-input memoryless output-symmetric channels. Our approach is based on analyzing the dynamics of the un-polarized channels. More precisely, we provide bounds on (the exponent of) the number of subchannels whose Bhattacharyya constant falls in a fixed interval [a, b]. Mathematically, this can be stated as bounding the sequence {1/n logPr(Z#N# n#N# ∈ [a, b])}#N# n∈N#N#, where Z#N# n#N# is the Bhattacharyya process. We then use these bounds to derive tradeoffs between the rate and the block-length. The main results of this paper can be summarized as follows. Consider the sum of Bhattacharyya parameters of subchannels chosen (by the polar coding scheme) to transmit information. If we require this sum to be smaller than a given value P#N# e#N# > 0, then the required block-length N scales in terms of the rate R <; I(W) as N ≥ α/(I(W) - R)#N# μ#N#, where α is a positive constant that depends on P#N# e#N# and I(W). We show that μ = 3.579 is a valid choice, and we conjecture that indeed the value of μ can be improved to μ = 3.627, the parameter for the binary erasure channel. Also, we show that with the same requirement on the sum of Bhattacharyya parameters, the blocklength scales in terms of the rate like N ≤ β/(I(W) - R)#N# μ#N#, where β is a constant that depends on P#N# e#N# and I(W), and μ = 6.

Connected through hyperedge HE-1 (induced by source paper "reed muller codes theory and algorithms"):
  with members:
    - "reed muller codes polarize"
    - "recursive projection aggregation decoding of reed muller codes"
    - "general strong polarization"
    - "on the performance of reed muller codes with respect to random errors and erasures"
    - "decoding reed muller codes using minimum weight parity checks"
    - "on the bias of reed muller codes over odd prime fields"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "soft decision decoding of reed muller codes recursive lists"

Connected through hyperedge HE-2 (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"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "list decoding of polar codes"
    - "polar codes characterization of exponent bounds and constructions"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "reed muller codes polarize"):
  with members:
    - "recursive projection aggregation decoding of reed muller codes"
    - "general strong polarization"
    - "on the performance of reed muller codes with respect to random errors and erasures"
    - "decoding reed muller codes using minimum weight parity checks"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "recursive decoding and its performance for low rate reed muller codes"
    - "algebraic properties of polar codes from a new polynomial formalism"
    - "high girth matrices and polarization"

Connected through hyperedge HE-6 (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"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "polar codes speed of polarization and polynomial gap to capacity"

Connected through hyperedge HE-7 (induced by source paper "coded distributed computing performance limits and code designs"):
  with members:
    - "polar coded distributed matrix multiplication"
    - "speeding up distributed machine learning using codes"
    - "a low complexity recursive approach toward code domain noma for massive communications"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "coded sparse matrix multiplication"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-8 (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"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "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)