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 general framework for symmetric property estimation"):
  Abstract: In this paper we provide a general framework for estimating symmetric properties of distributions from i.i.d. samples. For a broad class of symmetric properties we identify the easy region where empirical estimation works and the difficult region where more complex estimators are required. We show that by approximately computing the profile maximum likelihood (PML) distribution \cite{ADOS16} in this difficult region we obtain a symmetric property estimation framework that is sample complexity optimal for many properties in a broader parameter regime than previous universal estimation approaches based on PML. The resulting algorithms based on these pseudo PML distributions are also more practical.

Includes members:
  Member "efficient profile maximum likelihood for universal symmetric property estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating entropy of distributions in constant space")
  Member "approximate profile maximum likelihood":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient profile maximum likelihood for universal symmetric property estimation")
  Member "estimating the unseen from multiple populations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient profile maximum likelihood for universal symmetric property estimation")
  Member "a unified maximum likelihood approach for optimal distribution property estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient profile maximum likelihood for universal symmetric property estimation")
  Member "minimax estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "minimax rates of entropy estimation on large alphabets via best polynomial approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "to split or not to split the impact of disparate treatment in classification")

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)