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: "improved lower bounds for the fourier entropy influence conjecture via lexicographic functions"
  Abstract: Every Boolean function can be uniquely represented as a multilinear polynomial. The entropy and the total influence are two ways to measure the concentration of its Fourier coefficients, namely the monomial coefficients in this representation: the entropy roughly measures their spread, while the total influence measures their average level. The Fourier Entropy/Influence conjecture of Friedgut and Kalai from 1996 states that the entropy to influence ratio is bounded by a universal constant $C$. #R##N#Using lexicographic Boolean functions, we present three explicit asymptotic constructions that improve upon the previously best known lower bound $C>6.278944$ by O'Donnell and Tan, obtained via recursive composition. The first uses their construction with the lexicographic function $\ell\left\langle 2/3\right\rangle $ of measure $2/3$ to demonstrate that $C\ge4+3\log_{4}3>6.377444$. The second generalizes their construction to biased functions and obtains $C>6.413846$ using $\ell\left\langle \Phi\right\rangle $, where $\Phi$ is the inverse golden ratio. The third, independent, construction gives $C>6.454784$, even for monotone functions. #R##N#Beyond modest improvements to the value of $C$, our constructions shed some new light on the properties sought in potential counterexamples to the conjecture. #R##N#Additionally, we prove a Lipschitz-type condition on the total influence and spectral entropy, which may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "improved bounds on fourier entropy and min entropy"):
  with members:
    - "on the fourier entropy influence conjecture for extremal classes"
    - "nearly optimal separations between communication or query complexity and partitions"
    - "low sensitivity functions from unambiguous certificates"
    - "degree and sensitivity tails of two distributions"
    - "polynomial bounds for decoupling with applications"
    - "almost quadratic gap between partition complexity and query communication complexity"
    - "analysis of boolean functions"
    - "a composition theorem for parity kill number"

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)