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: "binary linear codes with optimal scaling polar codes with large kernels"
  Abstract: We prove that, at least for the binary erasure channel (BEC), the polar-coding paradigm gives rise to codes that not only approach the Shannon limit but do so under the best possible scaling of their block length as a function of the gap to capacity. This result exhibits the first known family of binary codes that attain both optimal scaling and quasi-linear complexity of construction, encoding and decoding. Specifically, for any fixed $\delta > 0$, we exhibit binary linear codes that ensure reliable communication at rates within $\varepsilon > 0$ of capacity with block length $n=O(1/\varepsilon^{2+\delta})$, construction complexity $\Theta(n)$, and encoding/decoding complexity $\Theta(n\log n)$. #R##N#Our proof is based on the construction and analysis of binary polar codes with large kernels. It was recently shown that, for all binary-input symmetric memoryless channels, conventional polar codes (based on a $2\times 2$ kernel) allow reliable communication at rates within $\varepsilon > 0$ of the Shannon capacity with block length, construction, encoding and decoding complexity all bounded by a polynomial in $1/\varepsilon$. In particular, this means that the block length $n$ scales as $O(1/\varepsilon^{\mu})$, where the constant $\mu$ is called the scaling exponent. It is furthermore known that the optimal scaling exponent is $\mu=2$, and it is achieved by random linear codes. However, for general channels, the decoding complexity of random linear codes is exponential in the block length. As far as conventional polar codes, their scaling exponent depends on the channel, and for the binary erasure channel it is given by $\mu=3.63$. #R##N#Our main contribution is a rigorous proof of the following result: there exist $\ell\times\ell$ binary kernels, such that polar codes constructed from these kernels achieve scaling exponent $\mu(\ell)$ that tends to the optimal value of $2$ as $\ell$ grows.

Connected through hyperedge HE-1 (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-2 (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"
    - "partitioned successive cancellation list decoding of polar codes"
    - "construction of polar codes with sublinear complexity"

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 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"
    - "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-4 (induced by source paper "efficient decoding of polar codes with some 16 16 kernels"):
  with members:
    - "a randomized construction of polar subcodes"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "binary polarization kernels from code decompositions"
    - "scaling exponent of list decoders with applications to polar codes"
    - "list decoding of polar codes"
    - "polar codes characterization of exponent bounds and constructions"

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)