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 "wasserstein identity testing"):
  Abstract: Uniformity testing and the more general identity testing are well studied problems in distributional property testing. Most previous work focuses on testing under $L_1$-distance. However, when the support is very large or even continuous, testing under $L_1$-distance may require a huge (even infinite) number of samples. Motivated by such issues, we consider the identity testing in Wasserstein distance (a.k.a. transportation distance and earthmover distance) on a metric space (discrete or continuous). #R##N#In this paper, we propose the Wasserstein identity testing problem (Identity Testing in Wasserstein distance). We obtain nearly optimal worst-case sample complexity for the problem. Moreover, for a large class of probability distributions satisfying the so-called "Doubling Condition", we provide nearly instance-optimal sample complexity.

Includes members:
  Member "sharp bounds for generalized uniformity testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication and memory efficient testing of discrete distributions")
  Member "generalized uniformity testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication and memory efficient testing of discrete distributions")
  Member "which distribution distances are sublinearly testable":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private identity testing for high dimensional distributions")
  Member "a new approach for testing properties of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "private identity testing for high dimensional distributions")
  Member "learning arbitrary statistical mixtures of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal estimation of change in a population of parameters")
  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")

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.LG(Machine Learning)