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: "efficiently decoding reed muller codes from random errors"
  Abstract: Reed-Muller codes encode an $m$-variate polynomial of degree $r$ by evaluating it on all points in $\{0,1\}^m$. We denote this code by $RM(m,r)$. The minimal distance of $RM(m,r)$ is $2^{m-r}$ and so it cannot correct more than half that number of errors in the worst case. For random errors one may hope for a better result. #R##N#In this work we give an efficient algorithm (in the block length $n=2^m$) for decoding random errors in Reed-Muller codes far beyond the minimal distance. Specifically, for low rate codes (of degree $r=o(\sqrt{m})$) we can correct a random set of $(1/2-o(1))n$ errors with high probability. For high rate codes (of degree $m-r$ for $r=o(\sqrt{m/\log m})$), we can correct roughly $m^{r/2}$ errors. #R##N#More generally, for any integer $r$, our algorithm can correct any error pattern in $RM(m,m-(2r+2))$ for which the same erasure pattern can be corrected in $RM(m,m-(r+1))$. The results above are obtained by applying recent results of Abbe, Shpilka and Wigderson (STOC, 2015), Kumar and Pfister (2015) and Kudekar et al. (2015) regarding the ability of Reed-Muller codes to correct random erasures. #R##N#The algorithm is based on solving a carefully defined set of linear equations and thus it is significantly different than other algorithms for decoding Reed-Muller codes that are based on the recursive structure of the code. It can be seen as a more explicit proof of a result of Abbe et al. that shows a reduction from correcting erasures to correcting errors, and it also bares some similarities with the famous Berlekamp-Welch algorithm for decoding Reed-Solomon codes.

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 "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-3 (induced by source paper "recursive projection aggregation decoding of reed muller codes"):
  with members:
    - "reed muller codes theory and algorithms"
    - "reed muller codes polarize"
    - "on the performance of reed muller codes with respect to random errors and erasures"
    - "decoding reed muller codes using minimum weight parity checks"
    - "decoding reed muller and polar codes by successive factor graph permutations"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "soft decision decoding of reed muller codes recursive lists"
    - "recursive decoding and its performance for low rate reed muller codes"

Connected through hyperedge HE-4 (induced by source paper "polar codes with a stepped boundary"):
  with members:
    - "soft decision decoding of reed muller codes recursive lists"
    - "recursive constructions and their maximum likelihood decoding"
    - "algebraic properties of polar codes from a new polynomial formalism"
    - "reed muller codes for random erasures and errors"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "efficient multi point local decoding of reed muller codes via interleaved codex"):
  with members:
    - "efficiently list decodable punctured reed muller codes"
    - "efficient list decoding of punctured reed muller codes"
    - "high rate locally correctable codes via lifting"
    - "new affine invariant codes from lifting"

Connected through hyperedge HE-6 (induced by source paper "on multi point local decoding of reed muller codes"):
  with members:
    - "efficient list decoding of punctured reed muller codes"
    - "high rate locally correctable codes via lifting"
    - "new affine invariant codes from lifting"

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)