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 "l p testing and learning of discrete distributions"):
  Abstract: The classic problems of testing uniformity of and learning a discrete distribution, given access to independent samples from it, are examined under general lp metrics. The intuitions and results often contrast with the classic l1 case. For p > 1, we can learn and test with a number of samples that is independent of the support size of the distribution: For 1    For some lp metrics, uniformity testing becomes easier over larger supports: a 6-sided die requires fewer trials to test for fairness than a 2-sided coin, and a card-shuffler requires fewer trials than the die. In fact, this inverse dependence on support size holds if and only if p > 4/3. The uniformity testing algorithm simply thresholds the number of "collisions" or "coincidences" and has an optimal sample complexity up to constant factors for all 1 ≤ p ≤ 2. Another algorithm gives order-optimal sample complexity for l∞ uniformity testing. Meanwhile, the most natural learning algorithm is shown to have order-optimal sample complexity for all lp metrics.   The author thanks Cement Canonne for discussions and contributions to this work.

Includes members:
  Member "testing identity of structured distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "sublinear time algorithms for earth mover s distance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the gaussiansketch for almost relative error kernel distance")

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)