human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "message importance measure and its application to minority subset detection in big data"
  Abstract: Message importance measure (MIM) is an important index to describe the message importance in the scenario of big data. Similar to the Shannon Entropy and Renyi Entropy, MIM is required to characterize the uncertainty of a random process and some related statistical characteristics. Moreover, MIM also need to highlight the importance of those events with relatively small occurring probabilities, thereby is especially applicable to big data. In this paper, we first define a parametric MIM measure from the viewpoint of information theory and then investigate its properties. We also present a parameter selection principle that provides answers to the minority subsets detection problem in the statistical processing of big data.

Connected through hyperedge HE-1 (induced by source paper "storage space allocation strategy for digital data with message importance"):
  with members:
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "importance of small probability events in big data information measures applications and challenges"
    - "pareto optimal data compression for binary classification tasks"
    - "learning convolutional networks for content weighted image compression"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-3 (induced by source paper "matching users preference under target revenue constraints in data recommendation systems"):
  with members:
    - "information measure similarity theory message importance measure via shannon entropy"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "wireless communications in the era of big data"
    - "differentially private online learning for cloud based video recommendation with multimedia big data in social networks"
    - "optimal forgery and suppression of ratings for privacy enhancement in recommendation systems"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-4 (induced by source paper "information measure similarity theory message importance measure via shannon entropy"):
  with members:
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "a large deviations approach to secure lossy compression"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"

Connected through hyperedge HE-5 (induced by source paper "differential message importance measure a new approach to the required sampling number in big data structure characterization"):
  with members:
    - "non parametric message important measure storage code design and transmission planning for big data"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "sampling and distortion tradeoffs for indirect source retrieval"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-6 (induced by source paper "a switch to the concern of user importance coefficient in utility distribution and message importance measure"):
  with members:
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "wireless communications in the era of big data"
    - "renyi divergence and kullback leibler divergence"
    - "identifying the signs of fraudulent accounts using data mining techniques"

Connected through hyperedge HE-7 (induced by source paper "how many samples required in big data collection a differential message importance measure"):
  with members:
    - "non parametric message important measure storage code design and transmission planning for big data"
    - "amplifying inter message distance on information divergence measures in big data"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-8 (induced by source paper "state variation mining on information divergence with message importance in big data"):
  with members:
    - "identifying the signs of fraudulent accounts using data mining techniques"

Question: Please tell me which class the center node 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)