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 "a chasm between identity and equivalence testing with conditional queries"):
  Abstract: A recent model for property testing of probability distributions enables tremendous savings in the sample complexity of testing algorithms, by allowing them to condition the sampling on subsets of the domain. #R##N#In particular, Canonne, Ron, and Servedio showed that, in this setting, testing identity of an unknown distribution $D$ (i.e., whether $D=D^*$ for an explicitly known $D^*$) can be done with a constant number of samples, independent of the support size $n$ -- in contrast to the required $\sqrt{n}$ in the standard sampling model. However, it was unclear whether the same held for the case of testing equivalence, where both distributions are unknown. Indeed, while Canonne, Ron, and Servedio established a $\mathrm{poly}\log(n)$-query upper bound for equivalence testing, very recently brought down to $\tilde O(\log\log n)$ by Falahatgar et al., whether a dependence on the domain size $n$ is necessary was still open, and explicitly posed by Fischer at the Bertinoro Workshop on Sublinear Algorithms. In this work, we answer the question in the positive, showing that any testing algorithm for equivalence must make $\Omega(\sqrt{\log\log n})$ queries in the conditional sampling model. Interestingly, this demonstrates an intrinsic qualitative gap between identity and equivalence testing, absent in the standard sampling model (where both problems have sampling complexity $n^{\Theta(1)}$). #R##N#Turning to another question, we investigate the complexity of support size estimation. We provide a doubly-logarithmic upper bound for the adaptive version of this problem, generalizing work of Ron and Tsur to our weaker model. We also establish a logarithmic lower bound for the non-adaptive version of this problem. This latter result carries on to the related problem of non-adaptive uniformity testing, an exponential improvement over previous results that resolves an open question of Chakraborty et al.

Includes members:
  Member "testing probability distributions underlying aggregated data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins")
  Member "the power of an example hidden set size approximation using group queries and conditional sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal randomized group testing algorithm to determine the number of defectives")
  Member "optimal algorithms for testing closeness of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "testing probability distributions using conditional samples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "on the power of conditional samples in distribution testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "testing closeness of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "streaming and sublinear approximation of entropy and information distances":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins")

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)