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: "estimating the distance from testable affine invariant properties"
  Abstract: Let $\cal{P}$ be an affine invariant property of functions $\mathbb{F}_p^n \to [R]$ for fixed $p$ and $R$. We show that if $\cal{P}$ is locally testable with a constant number of queries, then one can estimate the distance of a function $f$ from $\cal{P}$ with a constant number of queries. This was previously unknown even for simple properties such as cubic polynomials over $\mathbb{F}_2$. #R##N#Our test is simple: take a restriction of $f$ to a constant dimensional affine subspace, and measure its distance from $\cal{P}$. We show that by choosing the dimension large enough, this approximates with high probability the global distance of $f$ from $\cP$. The analysis combines the approach of Fischer and Newman [SIAM J. Comp 2007] who established a similar result for graph properties, with recently developed tools in higher order Fourier analysis, in particular those developed in Bhattacharyya et al. [STOC 2013].

Connected through hyperedge HE-1 (induced by source paper "fast fourier sparsity testing"):
  with members:
    - "robust estimators in high dimensions without the computational intractability"
    - "analysis of boolean functions"
    - "fourier sparsity spectral norm and the log rank conjecture"
    - "on the structure of boolean functions with small spectral norm"
    - "nearly optimal sparse fourier transform"
    - "on the parity complexity measures of boolean functions"
    - "on the communication complexity of xor functions"

Connected through hyperedge HE-2 (induced by source paper "using higher order fourier analysis over general fields"):
  with members:
    - "a characterization of locally testable affine invariant properties via decomposition theorems"
    - "algorithmic regularity for polynomials and applications"
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "a unified framework for testing linear invariant properties"
    - "succinct representation of codes with applications to testing"

Connected through hyperedge HE-3 (induced by source paper "testing properties of functions on finite groups"):
  with members:
    - "a characterization of locally testable affine invariant properties via decomposition theorems"
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "partially symmetric functions are efficiently isomorphism 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"
    - "list decoding reed muller codes over small fields"
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "a unified framework for testing linear invariant properties"

Connected through hyperedge HE-5 (induced by source paper "a characterization of locally testable affine invariant properties via decomposition theorems"):
  with members:
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "partially symmetric functions are efficiently isomorphism testable"
    - "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)