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: "near log convexity of measured heat in discrete time and consequences"
  Abstract: Let $u,v \in \mathbb{R}^\Omega_+$ be positive unit vectors and $S\in\mathbb{R}^{\Omega\times\Omega}_+$ be a symmetric substochastic matrix. For an integer $t\ge 0$, let $m_t = \smash{\left\langle v,S^tu\right\rangle}$, which we view as the heat measured by $v$ after an initial heat configuration $u$ is let to diffuse for $t$ time steps according to $S$. Since $S$ is entropy improving, one may intuit that $m_t$ should not change too rapidly over time. We give the following formalizations of this intuition. #R##N#We prove that $m_{t+2} \ge m_t^{1+2/t}\!,$ an inequality studied earlier by Blakley and Dixon (also Erdős and Simonovits) for $u=v$ and shown true under the restriction $m_t\ge e^{-4t}$. Moreover we prove that for any $\epsilon>0$, a stronger inequality $m_{t+2} \ge t^{1-\epsilon}\cdot \smash{m_t^{1+2/t}}$ holds unless $m_{t+2}m_{t-2}\ge \delta m_t^2$ for some $\delta$ that depends on $\epsilon$ only. Phrased differently, $\forall \epsilon> 0, \exists \delta> 0$ such that $\forall S,u,v$ \begin{equation*} \frac{m_{t+2}}{m_{t}^{1+2/t}}\ge \min\left\{t^{1-\epsilon}, \delta\frac{m_t^{1-2/t}}{m_{t-2}}\right\}, \quad \forall t \ge 2, \end{equation*} which can be viewed as a truncated log-convexity statement. #R##N#Using this inequality, we answer two related open questions in complexity theory: Any property tester for $k$-linearity requires $\Omega(k\log k)$ queries and the randomized communication complexity of the $k$-Hamming distance problem is $\Omega(k\log k)$. Further we show that any randomized parity decision tree computing $k$-Hamming weight has size $\exp\left(\Omega(k\log k)\right)$.

Connected through hyperedge HE-1 (induced by source paper "almost optimal testers for concise representations"):
  with members:
    - "almost optimal distribution free junta testing"
    - "distribution free junta testing"
    - "exact learning from an honest teacher that answers membership queries"
    - "exact learning of juntas from membership queries"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "testing unateness of real valued functions"
    - "an widetilde o n queries adaptive tester for unateness"

Connected through hyperedge HE-2 (induced by source paper "universal communication universal graphs and graph labeling"):
  with members:
    - "the power of shared randomness in uncertain communication"
    - "an optimal ancestry labeling scheme with applications to xml trees and universal posets"
    - "on the implicit graph conjecture"
    - "communication with contextual uncertainty"
    - "optimal induced universal graphs and adjacency labeling for trees"
    - "contact representations of graphs in 3d"
    - "sublinear distance labeling"
    - "distance labeling schemes for trees"

Connected through hyperedge HE-3 (induced by source paper "almost optimal distribution free junta testing"):
  with members:
    - "exact learning of juntas from membership queries"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "testing unateness of real valued functions"
    - "a polynomial lower bound for testing monotonicity"
    - "tight bounds for the distribution free testing of monotone conjunctions"
    - "new algorithms and lower bounds for monotonicity testing"
    - "a o n monotonicity tester for boolean functions over the hypercube"

Connected through hyperedge HE-4 (induced by source paper "junta correlation is testable"):
  with members:
    - "distribution free junta testing"
    - "boolean function analysis meets stochastic optimization an approximation scheme for stochastic knapsack"
    - "settling the query complexity of non adaptive junta testing"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "analysis of boolean functions"

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)