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: "reed muller codes for random erasures and errors"
  Abstract: This paper studies the parameters for which Reed-Muller (RM) codes over $GF(2)$ can correct random erasures and random errors with high probability, and in particular when can they achieve capacity for these two classical channels. Necessarily, the paper also studies properties of evaluations of multi-variate $GF(2)$ polynomials on random sets of inputs. #R##N#For erasures, we prove that RM codes achieve capacity both for very high rate and very low rate regimes. For errors, we prove that RM codes achieve capacity for very low rate regimes, and for very high rates, we show that they can uniquely decode at about square root of the number of errors at capacity. #R##N#The proofs of these four results are based on different techniques, which we find interesting in their own right. In particular, we study the following questions about $E(m,r)$, the matrix whose rows are truth tables of all monomials of degree $\leq r$ in $m$ variables. What is the most (resp. least) number of random columns in $E(m,r)$ that define a submatrix having full column rank (resp. full row rank) with high probability? We obtain tight bounds for very small (resp. very large) degrees $r$, which we use to show that RM codes achieve capacity for erasures in these regimes. #R##N#Our decoding from random errors follows from the following novel reduction. For every linear code $C$ of sufficiently high rate we construct a new code $C'$, also of very high rate, such that for every subset $S$ of coordinates, if $C$ can recover from erasures in $S$, then $C'$ can recover from errors in $S$. Specializing this to RM codes and using our results for erasures imply our result on unique decoding of RM codes at high rate. #R##N#Finally, two of our capacity achieving results require tight bounds on the weight distribution of RM codes. We obtain such bounds extending the recent \cite{KLP} bounds from constant degree to linear degree polynomials.

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 "an upper bound on ell_q norms of noisy functions"):
  with members:
    - "on the entropy of a noisy function"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (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"
    - "efficiently decoding reed muller codes from random 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-6 (induced by source paper "enhanced recursive reed muller erasure decoding"):
  with members:
    - "soft decision decoding of reed muller codes recursive lists"
    - "decoding high rate reed muller codes from random errors in near linear time"
    - "permutation decoding and the stopping redundancy hierarchy of cyclic and extended cyclic codes"

Connected through hyperedge HE-7 (induced by source paper "comparing the bit map and block map decoding thresholds of reed muller codes on bms channels"):
  with members:
    - "reed muller codes achieve capacity on erasure channels"
    - "channel coding the road to channel capacity"

Connected through hyperedge HE-8 (induced by source paper "reed muller codes achieve capacity on erasure channels"):
  with members:
    - "soft decision decoding of reed muller codes recursive lists"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "cyclic coded integer forcing equalization"
    - "channel coding the road to channel capacity"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the generalized area theorem and some of its consequences"

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)