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: "sensitivity affine transforms and quantum communication complexity"
  Abstract: $\newcommand{\F}{\mathbb{F}} $We study the Boolean function parameters sensitivity ($s$), block sensitivity ($bs$), and alternation ($alt$) under specially designed affine transforms. For a function $f:\F_2^n \to \{0,1\}$, and $A = Mx+b$ for $M \in \F_2^{n \times n}$ and $b \in \F_2^n$, the result of the transformation $g$ is defined as $\forall x \in \F_2^n, g(x) = f(Mx+b)$. #R##N#We study alternation under linear shifts (when $M$ is the identity matrix) called the shift invariant alternation (denoted by $salt(f)$). By a result of Lin and Zhang (2017), it follows that $bs(f) \le O(salt(f)^2s(f))$. Thus, to settle the Sensitivity Conjecture, it suffices to argue that $\forall~f, salt(f) \le poly(s(f))$. However, we exhibit an explicit family of functions for which $salt(f)$ is $2^{\Omega(s(f))}$. #R##N#We show an affine transform $A$, such that the corresponding function $g$ satisfies $bs(f,0^n) \le s(g)$, using which we proving that for $F(x,y) := f(x \land y)$, the bounded error quantum communication complexity of $F$ with prior entanglement, $Q^*_{1/3}(F) = \Omega(\sqrt{bs(f,0^n)})$. Our proof builds on ideas from Sherstov (2010) where we use specific properties of the above affine transformation. Using this, we show, #R##N#* For a fixed prime $p$ and $0 < \epsilon < 1$, any $f$ with $deg_p(f) \le (1-\epsilon)\log n$ must satisfy $Q^*_{1/3}(F) = \Omega\left (\frac{n^{\epsilon/2}}{\log n} \right )$. Here, $deg_p(f)$ denotes the degree of the multilinear polynomial of $f$ over $\F_p$ . #R##N#* For $f$ such that there exists primes $p$ and $q$ with $deg_q(f) \ge \Omega(deg_p(f)^\delta)$ for $\delta > 2$, the deterministic communication complexity - $D(F)$ and $Q^*_{1/3}(F)$ are polynomially related. In particular, this holds when $deg_p(f) = O(1)$. Thus, for this class of functions, this answers an open question (see Buhrman and deWolf (2001)) about the relation between the two measures.

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)