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 "importance of small probability events in big data information measures applications and challenges"):
  Abstract: In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information of big data collected by Internet of Things (IoTs). That is, it is pretty crucial to explore the valuable information associated with the rare events involved in minority subsets of the voluminous amounts of data. To do so, how to effectively measure the information with importance of the small probability events from the perspective of information theory is a fundamental question. This paper first makes a survey of some theories and models with respect to importance measures and investigates the relationship between subjective or semantic importance and rare events in big data. Moreover, some applications for message processing and data analysis are discussed in the viewpoint of information measures. In addition, based on rare events detection, some open challenges related to information measures, such as smart cities, autonomous driving, and anomaly detection in IoTs, are introduced which can be considered as future research directions.

Includes members:
  Member "local deep feature alignment for unsupervised dimension reduction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving variational autoencoder with deep feature consistent and generative adversarial training")
  Member "matching users preference under target revenue constraints in data recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "storage space allocation strategy for digital data with message importance")
  Member "differential message importance measure a new approach to the required sampling number in big data structure characterization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information measure similarity theory message importance measure via shannon entropy")
  Member "decorrelation of neutral vector variables theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering")
  Member "group cooperation with optimal resource allocation in wireless powered communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fog assisted multi user swipt networks local computing or offloading")
  Member "amplifying inter message distance on information divergence measures in big data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differential message importance measure a new approach to the required sampling number in big data structure characterization")
  Member "focusing on a probability element parameter selection of message importance measure in big data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matching users preference under target revenue constraints in data recommendation systems")
  Member "linear complexity exponentially consistent tests for universal outlying sequence detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nonparametric detection of anomalous data streams")

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)