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: "a composition theorem for the fourier entropy influence conjecture"
  Abstract: The Fourier Entropy-Influence (FEI) conjecture of Friedgut and Kalai [FK96] seeks to relate two fundamental measures of Boolean function complexity: it states that $H[f] \leq C Inf[f]$ holds for every Boolean function $f$, where $H[f]$ denotes the spectral entropy of $f$, $Inf[f]$ is its total influence, and $C > 0$ is a universal constant. Despite significant interest in the conjecture it has only been shown to hold for a few classes of Boolean functions. #R##N#Our main result is a composition theorem for the FEI conjecture. We show that if $g_1,...,g_k$ are functions over disjoint sets of variables satisfying the conjecture, and if the Fourier transform of $F$ taken with respect to the product distribution with biases $E[g_1],...,E[g_k]$ satisfies the conjecture, then their composition $F(g_1(x^1),...,g_k(x^k))$ satisfies the conjecture. As an application we show that the FEI conjecture holds for read-once formulas over arbitrary gates of bounded arity, extending a recent result [OWZ11] which proved it for read-once decision trees. Our techniques also yield an explicit function with the largest known ratio of $C \geq 6.278$ between $H[f]$ and $Inf[f]$, improving on the previous lower bound of 4.615.

Connected through hyperedge HE-1 (induced by source paper "revisiting bourgain kalai and fourier entropies"):
  with members:
    - "on the fourier entropy influence conjecture for extremal classes"
    - "improved bounds on fourier entropy and min entropy"
    - "comparing the bit map and block map decoding thresholds of reed muller codes on bms channels"
    - "analysis of boolean functions"

Connected through hyperedge HE-2 (induced by source paper "fourier entropy influence conjecture for random linear threshold functions"):
  with members:
    - "analysis of boolean functions"
    - "every decision tree has an influential variable"

Connected through hyperedge HE-3 (induced by source paper "improved bounds on fourier entropy and min entropy"):
  with members:
    - "on the fourier entropy influence conjecture for extremal classes"
    - "improved lower bounds for the fourier entropy influence conjecture via lexicographic functions"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on the fourier entropy influence conjecture for extremal classes"):
  with members:
    - "degree and sensitivity tails of two distributions"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (induced by source paper "low sensitivity functions from unambiguous certificates"):
  with members:
    - "randomized query complexity of sabotaged and composed functions"
    - "nearly optimal separations between communication or query complexity and partitions"
    - "degree and sensitivity tails of two distributions"
    - "separations in query complexity based on pointer functions"
    - "smooth boolean functions are easy efficient algorithms for low sensitivity functions"
    - "sensitivity versus certificate complexity of boolean functions"
    - "size of sets with small sensitivity a generalization of simon s lemma"
    - "an o n 0 4732 upper bound on the complexity of the gks communication game"

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)