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: "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.

Connected through hyperedge HE-1 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "learning populations of parameters"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "a data quality metric dqm how to estimate the number of undetected errors in data sets"
    - "a polynomial lower bound for testing monotonicity"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-2 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing"):
  with members:
    - "generalized uniformity testing"
    - "which distribution distances are sublinearly testable"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "faster sublinear algorithms using conditional sampling"
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"

Connected through hyperedge HE-3 (induced by source paper "property testing of joint distributions using conditional samples"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "faster sublinear algorithms using conditional sampling"
    - "testing bayesian networks"
    - "faster algorithms for testing under conditional sampling"
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"

Connected through hyperedge HE-4 (induced by source paper "improving and extending the testing of distributions for shape restricted properties"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "optimal testing for properties of distributions"
    - "testing shape restrictions of discrete distributions"
    - "testing probability distributions using conditional samples"
    - "on the power of conditional samples in distribution testing"

Connected through hyperedge HE-5 (induced by source paper "faster sublinear algorithms using conditional sampling"):
  with members:
    - "on the power of conditional samples in distribution testing"

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)