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 "non parametric message important measure storage code design and transmission planning for big data"):
  Abstract: Storage and transmission in big data are discussed in this paper, where message importance is taken into account. Similar to Shannon Entropy and Renyi Entropy, we define non-parametric message important measure (NMIM) as a measure for the message importance in the scenario of big data, which can characterize the uncertainty of random events. It is proved that the proposed NMIM can sufficiently describe two key characters of big data: rare events finding and large diversities of events. Based on NMIM, we first propose an effective compressed encoding mode for data storage, and then discuss the channel transmission over some typical channel models. Numerical simulation results show that using our proposed strategy occupies less storage space without losing too much message importance, and there are growth region and saturation region for the maximum transmission, which contributes to designing of better practical communication system.

Includes members:
  Member "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "lossy compression for compute and forward in limited backhaul uplink multicell processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "storage space allocation strategy for digital data with message importance")
  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 "distributed binary detection with lossy data compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy aware distributed hypothesis testing")
  Member "wireless communications in the era of big data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "artificial intelligence enabled wireless networking for 5g and beyond recent advances and future challenges")
  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")
  Member "the deterministic time linearity of service provided by fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a queueing characterization of information transmission over block fading rayleigh channels in the low snr regime")
  Member "identifying the signs of fraudulent accounts using data mining techniques":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")

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)