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 "which distribution distances are sublinearly testable"):
  Abstract: Given samples from an unknown distribution $p$ and a description of a distribution $q$, are $p$ and $q$ close or far? This question of "identity testing" has received significant attention in the case of testing whether $p$ and $q$ are equal or far in total variation distance. However, in recent work, the following questions have been been critical to solving problems at the frontiers of distribution testing: #R##N#-Alternative Distances: Can we test whether $p$ and $q$ are far in other distances, say Hellinger? #R##N#-Tolerance: Can we test when $p$ and $q$ are close, rather than equal? And if so, close in which distances? #R##N#Motivated by these questions, we characterize the complexity of distribution testing under a variety of distances, including total variation, $\ell_2$, Hellinger, Kullback-Leibler, and $\chi^2$. For each pair of distances $d_1$ and $d_2$, we study the complexity of testing if $p$ and $q$ are close in $d_1$ versus far in $d_2$, with a focus on identifying which problems allow strongly sublinear testers (i.e., those with complexity $O(n^{1 - \gamma})$ for some $\gamma > 0$ where $n$ is the size of the support of the distributions $p$ and $q$). We provide matching upper and lower bounds for each case. We also study these questions in the case where we only have samples from $q$ (equivalence testing), showing qualitative differences from identity testing in terms of when tolerance can be achieved. Our algorithms fall into the classical paradigm of $\chi^2$-statistics, but require crucial changes to handle the challenges introduced by each distance we consider. Finally, we survey other recent results in an attempt to serve as a reference for the complexity of various distribution testing problems.

Includes members:
  Member "a new approach for testing properties of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private identity testing for high dimensional distributions")
  Member "collision based testers are optimal for uniformity and closeness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "minimax rate optimal estimation of divergences between discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "credible sample elicitation")
  Member "optimal testing for properties of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "l p testing and learning of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing")
  Member "testing identity of structured distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "optimal algorithms for testing closeness of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "testing closeness of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")

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)