human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient quantum protocols for xor functions"):
  Abstract: We show that for any Boolean function f on {0,1}^n, the bounded-error quantum communication complexity of XOR functions $f\circ \oplus$ satisfies that $Q_\epsilon(f\circ \oplus) = O(2^d (\log\|\hat f\|_{1,\epsilon} + \log \frac{n}{\epsilon}) \log(1/\epsilon))$, where d is the F2-degree of f, and $\|\hat f\|_{1,\epsilon} = \min_{g:\|f-g\|_\infty \leq \epsilon} \|\hat f\|_1$. This implies that the previous lower bound $Q_\epsilon(f\circ \oplus) = \Omega(\log\|\hat f\|_{1,\epsilon})$ by Lee and Shraibman \cite{LS09} is tight for f with low F2-degree. The result also confirms the quantum version of the Log-rank Conjecture for low-degree XOR functions. In addition, we show that the exact quantum communication complexity satisfies $Q_E(f) = O(2^d \log \|\hat f\|_0)$, where $\|\hat f\|_0$ is the number of nonzero Fourier coefficients of f. This matches the previous lower bound $Q_E(f(x,y)) = \Omega(\log rank(M_f))$ by Buhrman and de Wolf \cite{BdW01} for low-degree XOR functions.

Includes members:
  Member "fourier sparsity spectral norm and the log rank conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "tight bounds on communication complexity of symmetric xor functions in one way and smp models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sensitivity conjecture and log rank conjecture for functions with small alternating numbers")
  Member "on the parity complexity measures of boolean functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "an optimal lower bound on the communication complexity of gap hamming distance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "on the communication complexity of xor functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "communication complexity lower bounds by polynomials":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sensitivity affine transforms and quantum communication complexity")

Question: Please tell me which class the source paper 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)