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 "justification of logarithmic loss via the benefit of side information"):
  Abstract: We consider a natural measure of relevance: the reduction in optimal prediction risk in the presence of side information. For any given loss function, this relevance measure captures the benefit of side information for performing inference on a random variable under this loss function. When such a measure satisfies a natural data processing property, and the random variable of interest has alphabet size greater than two, we show that it is uniquely characterized by the mutual information, and the corresponding loss function coincides with logarithmic loss. In doing so, our work provides a new characterization of mutual information, and justifies its use as a measure of relevance. When the alphabet is binary, we characterize the only admissible forms the measure of relevance can assume while obeying the specified data processing property. Our results naturally extend to measuring the causal influence between stochastic processes, where we unify different causality measures in the literature as instantiations of directed information.

Includes members:
  Member "information measures the curious case of the binary alphabet":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on a generalization of the jensen shannon divergence")
  Member "minimax estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "the complexity of estimating r enyi entropy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimax optimal additive functional estimation with discrete distribution slow divergence speed case")
  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")
  Member "universal estimation of directed information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator")
  Member "the relation between granger causality and directed information theory a review":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "measuring sample path causal influences with relative entropy")
  Member "improved information gain estimates for decision tree induction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "inspectre privately estimating the unseen")

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)