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: "amplifying inter message distance on information divergence measures in big data"
  Abstract: Message identification (M-I) divergence is an important measure of the information distance between probability distributions, similar to Kullback-Leibler (K-L) and Renyi divergence. In fact, M-I divergence with a variable parameter can make an effect on characterization of distinction between two distributions. Furthermore, by choosing an appropriate parameter of M-I divergence, it is possible to amplify the information distance between adjacent distributions while maintaining enough gap between two nonadjacent ones. Therefore, M-I divergence can play a vital role in distinguishing distributions more clearly. In this paper, we first define a parametric M-I divergence in the view of information theory and then present its major properties. In addition, we design a M-I divergence estimation algorithm by means of the ensemble estimator of the proposed weight kernel estimators, which can improve the convergence of mean squared error from ${O(\varGamma^{-j/d})}$ to ${O(\varGamma^{-1})}$ $({j\in (0,d]})$. We also discuss the decision with M-I divergence for clustering or classification, and investigate its performance in a statistical sequence model of big data for the outlier detection problem.

Connected through hyperedge HE-1 (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"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"
    - "a large deviations approach to secure lossy compression"

Connected through hyperedge HE-2 (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"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "message importance measure and its application to minority subset detection in big data"
    - "sampling and distortion tradeoffs for indirect source retrieval"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-3 (induced by source paper "a switch to the concern of user importance coefficient in utility distribution and message importance measure"):
  with members:
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "message importance measure and its application to minority subset detection 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-4 (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"
    - "message importance measure and its application to minority subset detection in big data"
    - "renyi divergence and kullback leibler divergence"

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)