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 optimal adjacency labeling scheme for power law graphs"
  Abstract: An adjacency labeling scheme is a method that assigns labels to the vertices of a graph such that adjacency between vertices can be inferred directly from the assigned label, without using a centralized data structure. We devise adjacency labeling schemes for the family of power-law graphs. This family that has been used to model many types of networks, e.g. the Internet AS-level graph. Furthermore, we prove an almost matching lower bound for this family. We also provide an asymptotically near- optimal labeling scheme for sparse graphs. Finally, we validate the efficiency of our labeling scheme by an experimental evaluation using both synthetic data and real-world networks of up to hundreds of thousands of vertices.

Connected through hyperedge HE-1 (induced by source paper "shorter labels for routing in trees"):
  with members:
    - "near optimal compression for the planar graph metric"
    - "a note on distance labeling in planar graphs"
    - "optimal distance labeling schemes for trees"
    - "optimal induced universal graphs and adjacency labeling for trees"
    - "simpler faster and shorter labels for distances in graphs"
    - "sublinear space distance labeling using hubs"
    - "distance labeling schemes for trees"
    - "a simple and optimal ancestry labeling scheme for trees"

Connected through hyperedge HE-2 (induced by source paper "optimal distance labeling schemes for trees"):
  with members:
    - "optimal induced universal graphs and adjacency labeling for trees"
    - "simpler faster and shorter labels for distances in graphs"
    - "adjacency labeling schemes and induced universal graphs"
    - "near optimal labeling schemes for nearest common ancestors"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "new lower and upper bounds for representing sequences"

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.DC(Distributed, Parallel, and Cluster Computing)