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 "universal communication universal graphs and graph labeling"):
  Abstract: We introduce a communication model called universal SMP, in which Alice and Bob receive a function $f$ belonging to a family $\mathcal{F}$, and inputs $x$ and $y$. Alice and Bob use shared randomness to send a message to a third party who cannot see $f, x, y$, or the shared randomness, and must decide $f(x,y)$. Our main application of universal SMP is to relate communication complexity to graph labeling, where the goal is to give a short label to each vertex in a graph, so that adjacency or other functions of two vertices $x$ and $y$ can be determined from the labels $\ell(x),\ell(y)$. We give a universal SMP protocol using $O(k^2)$ bits of communication for deciding whether two vertices have distance at most $k$ on distributive lattices (generalizing the $k$-Hamming Distance problem in communication complexity), and explain how this implies an $O(k^2\log n)$ labeling scheme for determining $\mathrm{dist}(x,y) \leq k$ on distributive lattices with size $n$; in contrast, we show that a universal SMP protocol for determining $\mathrm{dist}(x,y) \leq 2$ in modular lattices (a superset of distributive lattices) has super-constant $\Omega(n^{1/4})$ communication cost. On the other hand, we demonstrate that many graph families known to have efficient adjacency labeling schemes, such as trees, low-arboricity graphs, and planar graphs, admit constant-cost communication protocols for adjacency. Trees also have an $O(k)$ protocol for deciding $\mathrm{dist}(x,y) \leq k$ and planar graphs have an $O(1)$ protocol for $\mathrm{dist}(x,y) \leq 2$, which implies a new $O(\log n)$ labeling scheme for the same problem on planar graphs.

Includes members:
  Member "near log convexity of measured heat in discrete time and consequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "almost optimal testers for concise representations")
  Member "the power of shared randomness in uncertain communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "round complexity of common randomness generation the amortized setting")
  Member "an optimal ancestry labeling scheme with applications to xml trees and universal posets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a complexity theory for labeling schemes")
  Member "on the implicit graph conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations")
  Member "communication with contextual uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the power of shared randomness in uncertain communication")
  Member "optimal induced universal graphs and adjacency labeling for trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "shorter labels for routing in trees")
  Member "contact representations of graphs in 3d":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on graph drawing beyond planarity")
  Member "sublinear distance labeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adjacency labelling for planar graphs and beyond")

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)