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: "list decoding reed muller codes over small fields"
  Abstract: The list decoding problem for a code asks for the maximal radius up to which any ball of that radius contains only a constant number of codewords. The list decoding radius is not well understood even for well studied codes, like Reed-Solomon or Reed-Muller codes. #R##N#Fix a finite field $\mathbb{F}$. The Reed-Muller code $\mathrm{RM}_{\mathbb{F}}(n,d)$ is defined by $n$-variate degree-$d$ polynomials over $\mathbb{F}$. In this work, we study the list decoding radius of Reed-Muller codes over a constant prime field $\mathbb{F}=\mathbb{F}_p$, constant degree $d$ and large $n$. We show that the list decoding radius is equal to the minimal distance of the code. #R##N#That is, if we denote by $\delta(d)$ the normalized minimal distance of $\mathrm{RM}_{\mathbb{F}}(n,d)$, then the number of codewords in any ball of radius $\delta(d)-\varepsilon$ is bounded by $c=c(p,d,\varepsilon)$ independent of $n$. This resolves a conjecture of Gopalan-Klivans-Zuckerman [STOC 2008], who among other results proved it in the special case of $\mathbb{F}=\mathbb{F}_2$; and extends the work of Gopalan [FOCS 2010] who proved the conjecture in the case of $d=2$. #R##N#We also analyse the number of codewords in balls of radius exceeding the minimal distance of the code. For $e \leq d$, we show that the number of codewords of $\mathrm{RM}_{\mathbb{F}}(n,d)$ in a ball of radius $\delta(e) - \varepsilon$ is bounded by $\exp(c \cdot n^{d-e})$, where $c=c(p,d,\varepsilon)$ is independent of $n$. The dependence on $n$ is tight. This extends the work of Kaufman-Lovett-Porat [IEEE Inf. Theory 2012] who proved similar bounds over $\mathbb{F}_2$. #R##N#The proof relies on several new ingredients: an extension of the Frieze-Kannan weak regularity to general function spaces, higher-order Fourier analysis, and an extension of the Schwartz-Zippel lemma to compositions of polynomials.

Connected through hyperedge HE-1 (induced by source paper "generalized list decoding"):
  with members:
    - "list decodable linear regression"
    - "list decodable learning via sum of squares"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "list decodable zero rate codes"
    - "an improved bound on the fraction of correctable deletions"
    - "upper bound on list decoding radius of binary codes"
    - "a characterization of the capacity of online causal binary channels"
    - "it ll probably work out improved list decoding through random operations"

Connected through hyperedge HE-2 (induced by source paper "decoding high rate reed muller codes from random errors in near linear time"):
  with members:
    - "recursive decoding and its performance for low rate reed muller codes"
    - "reed muller codes achieve capacity on erasure channels"
    - "reed muller codes achieve capacity on the binary erasure channel under map decoding"
    - "reed muller codes for random erasures and errors"
    - "from polar to reed muller codes a technique to improve the finite length performance"
    - "channel coding the road to channel capacity"

Connected through hyperedge HE-3 (induced by source paper "bias vs structure of polynomials in large fields and applications in effective algebraic geometry and coding theory"):
  with members:
    - "using higher order fourier analysis over general fields"
    - "algorithmic regularity for polynomials and applications"
    - "every locally characterized affine invariant property is testable"

Connected through hyperedge HE-4 (induced by source paper "gowers norm function limits and parameter estimation"):
  with members:
    - "bias vs structure of polynomials in large fields and applications in effective algebraic geometry and coding theory"
    - "using higher order fourier analysis over general fields"
    - "a characterization of locally testable affine invariant properties via decomposition theorems"
    - "estimating the distance from testable affine invariant properties"
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "a unified framework for testing linear invariant properties"

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.CC(Computational Complexity)