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 "matching users preference under target revenue constraints in data recommendation systems"):
  Abstract: This paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to systems with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.

Includes members:
  Member "information measure similarity theory message importance measure via shannon entropy":
    also connected through other hyperedges:
      (none)
  Member "group cooperation with optimal resource allocation in wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "focusing on a probability element parameter selection of message importance measure in big data":
    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 "message importance measure and its application to minority subset detection in big data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "storage space allocation strategy for digital data with message importance")
  Member "wireless communications in the era of big data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "artificial intelligence enabled wireless networking for 5g and beyond recent advances and future challenges")
  Member "differentially private online learning for cloud based video recommendation with multimedia big data in social networks":
    also connected through other hyperedges:
      (none)
  Member "optimal forgery and suppression of ratings for privacy enhancement in recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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)