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 "minimax optimal additive functional estimation with discrete distribution"):
  Abstract: This paper addresses a problem of estimating an additive functional given $n$ i.i.d. samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with alphabet size $k$. The additive functional is defined as $\theta(P;\phi)=\sum_{i=1}^k\phi(p_i)$ for a function $\phi$, which covers the most of the entropy-like criteria. The minimax optimal risk of this problem has been already known for some specific $\phi$, such as $\phi(p)=p^\alpha$ and $\phi(p)=-p\ln p$. However, there is no generic methodology to derive the minimax optimal risk for the additive function estimation problem. In this paper, we reveal the property of $\phi$ that characterizes the minimax optimal risk of the additive functional estimation problem; this analysis is applicable to general $\phi$. More precisely, we reveal that the minimax optimal risk of this problem is characterized by the divergence speed of the function $\phi$.

Includes members:
  Member "minimax optimal estimators for additive scalar functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating entropy of distributions in constant space")
  Member "does dirichlet prior smoothing solve the shannon entropy estimation problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "minimax estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "maximum likelihood estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "minimax rates of entropy estimation on large alphabets via best polynomial approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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.IT(Information Theory)