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: "almost optimal scaling of reed muller codes on bec and bsc channels"
  Abstract: Consider a binary linear code of length $N$, minimum distance $d_{\text{min}}$, transmission over the binary erasure channel with parameter $0  0$ if the minimum distance is large. In particular the width of the transition is of order $O(1/\sqrt{d_{\text{min}}})$. We strengthen this result by showing that under suitable conditions on the weight distribution of the code, the transition width can be as small as $\Theta(1/N^{\frac12-\kappa})$, for any $\kappa>0$, even if the minimum distance of the code is not linear. This condition applies e.g., to Reed-Mueller codes. Since $\Theta(1/N^{\frac12})$ is the smallest transition possible for any code, we speak of "almost" optimal scaling. We emphasize that the width of the transition says nothing about the location of the transition. Therefore this result has no bearing on whether a code is capacity-achieving or not. As a second contribution, we present a new estimate on the derivative of the EXIT function, the proof of which is based on the Blowing-Up Lemma.

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"
    - "soft decision decoding of reed muller codes recursive lists"
    - "syndrome decoding of reed muller codes and tensor decomposition over finite fields"

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"
    - "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"
    - "reed muller codes achieve capacity on erasure channels"

Connected through hyperedge HE-3 (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"
    - "coded sparse matrix multiplication"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"

Connected through hyperedge HE-4 (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"
    - "soft decision decoding of reed muller codes recursive lists"
    - "recursive decoding and its performance for low rate reed muller codes"
    - "fast list decoders for polar codes"

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)