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 "even simpler distance labeling for sparse graphs"):
  Abstract: We propose a labeling scheme for unweighted undirected graphs, that is an assignment of binary labels to vertices of a graph such that the distance between any pair of vertices can be decoded solely from their labels. Recently, Alstrup, Dahlgaard, Knudsen, and Porat (2015) have shown a labeling scheme for sparse graphs with labels of size $O(\frac{n}{D} \log^2 D)$ where $D = \frac{\log n}{\log \frac{m+n}{n}}$. We present a simpler approach achieving size $O(\frac{n}{D} \log D)$ with $O(n^\varepsilon)$ decoding time. Based on similar techniques we also present a scheme for general graphs with labels of size $\frac{\log 3}{2}n + o(n)$ with small decoding time (any $\omega(1)$ decoding time is possible), or labels of size $(\frac{\log 3}{2}+\varepsilon) n$ and decoding time $O(1)$. This almost matches the currently best scheme of Alstrup, Gavoille, Halvorsen, and Petersen (2015) which uses labels of size $\frac{\log 3}{2}n + o(n)$ with decoding time $O(1)$. We believe our simple techniques are of independent value and provide a desirable simplification of the previous approaches.

Includes members:
  Member "simpler faster and shorter labels for distances in graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shorter labels for routing in trees")
  Member "sublinear distance labeling for sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pruning based distance sketches with provable guarantees on random graphs")
  Member "approximate distance oracle with constant query time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "almost shortest paths and pram distance oracles in weighted graphs")
  Member "on the hardness of distance oracle for sparse graph":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast join project query evaluation using matrix multiplication")

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.DS(Data Structures and Algorithms)