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: "testing bipartitness in an augmented vdf bounded degree graph model"
  Abstract: In a recent work (ECCC, TR18-171, 2018), we introduced models of testing graph properties in which, in addition to answers to the usual graph-queries, the tester obtains {\em random vertices drawn according to an arbitrary distribution $D$}. Such a tester is required to distinguish between graphs that have the property and graphs that are far from having the property, {\em where the distance between graphs is defined based on the unknown vertex distribution $D$}. These ("vertex-distribution free" (VDF)) models generalize the standard models in which $D$ is postulated to be uniform on the vertex-set, and they were studies both in the dense graph model and in the bounded-degree graph model. #R##N#The focus of the aforementioned work was on testers, called {\sf strong}, whose query complexity depends only on the proximity parameter $\epsilon$. Unfortunately, in the standard bounded-degree graph model, some natural properties such as Bipartiteness do not have strong testers, and others (like cycle-freeness) do not have strong testers of one-sided error (whereas one-sided error was shown inherent to the VDF model). Hence, it was suggested to study general (i.e., non-strong) testers of "sub-linear" complexity. #R##N#In this work, we pursue the foregoing suggestion, but do so in a model that augments the model presented in the aforementioned work. Specifically, we provide the tester with an evaluation oracle to the unknown distribution $D$, in addition to samples of $D$ and oracle access to the tested graph. Our main results are testers for Bipartitness and cycle-freeness, in this augmented model, having complexity that is almost-linear in the square root of the "effective support size" of $D$.

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.DS(Data Structures and Algorithms)