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: "linear information coupling problems"
  Abstract: Many network information theory problems face the similar difficulty of single-letterization. We argue that this is due to the lack of a geometric structure on the space of probability distribution. In this paper, we develop such a structure by assuming that the distributions of interest are close to each other. Under this assumption, the K-L divergence is reduced to the squared Euclidean metric in an Euclidean space. In addition, we construct the notion of coordinate and inner product, which will facilitate solving communication problems. We will present the application of this approach to the point-to-point channel, general broadcast channel, and the multiple access channel (MAC) with the common source. It can be shown that with this approach, information theory problems, such as the single-letterization, can be reduced to some linear algebra problems. Moreover, we show that for the general broadcast channel, transmitting the common message to receivers can be formulated as the trade-off between linear systems. We also provide an example to visualize this trade-off in a geometric way. Finally, for the MAC with the common source, we observe a coherent combining gain due to the cooperation between transmitters, and this gain can be quantified by applying our technique.

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"
    - "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-2 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-3 (induced by source paper "an information theoretic approach to unsupervised feature selection for high dimensional data"):
  with members:
    - "an information theoretic interpretation to deep neural networks"
    - "on universal features for high dimensional learning and inference"
    - "discovering structure in high dimensional data through correlation explanation"

Connected through hyperedge HE-4 (induced by source paper "probabilistic clustering using maximal matrix norm couplings"):
  with members:
    - "linear bounds between contraction coefficients for f divergences"
    - "bounds between contraction coefficients"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "compressed secret key agreement maximizing multivariate mutual information per bit"):
  with members:
    - "secret key agreement under discussion rate constraints"
    - "opening the black box of deep neural networks via information"
    - "on the optimality of secret key agreement via omniscience"
    - "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model"
    - "duality between feature selection and data clustering"
    - "a lattice coding scheme for secret key generation from gaussian markov tree sources"
    - "common randomness and key generation with limited interaction"
    - "info clustering a mathematical theory for data clustering"

Connected through hyperedge HE-6 (induced by source paper "bounds between contraction coefficients"):
  with members:
    - "comparison of channels criteria for domination by a symmetric channel"
    - "f divergence inequalities"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "information extraction under privacy constraints"
    - "tight bounds for symmetric divergence measures and a new inequality relating f divergences"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"

Connected through hyperedge HE-7 (induced by source paper "euclidean information theory of networks"):
  with members:
    - "communication theoretic data analytics"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "wireless network information flow a deterministic approach"
    - "a coordinate system for gaussian networks"
    - "feedback capacity of the gaussian interference channel to within 2 bits"

Connected through hyperedge HE-8 (induced by source paper "on maximal correlation mutual information and data privacy"):
  with members:
    - "on the duality of additivity and tensorization"
    - "notes on information theoretic privacy"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"

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)