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: "jidt an information theoretic toolkit for studying the dynamics of complex systems"
  Abstract: Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.

Connected through hyperedge HE-1 (induced by source paper "information decomposition based on cooperative game theory"):
  with members:
    - "a novel approach to multivariate redundancy and synergy"
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "the partial entropy decomposition decomposing multivariate entropy and mutual information via pointwise common surprisal"
    - "multivariate dependence beyond shannon information"
    - "geometry of information integration"
    - "understanding interdependency through complex information sharing"
    - "hierarchical quantification of synergy in channels"
    - "reconsidering unique information towards a multivariate information decomposition"

Connected through hyperedge HE-2 (induced by source paper "infotheory a c python package for multivariate information theoretic analysis"):
  with members:
    - "idtxl the information dynamics toolkit xl a python package for the efficient analysis of multivariate information dynamics in networks"
    - "multivariate dependence beyond shannon information"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-3 (induced by source paper "itene intrinsic transfer entropy neural estimator"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "ccmi classifier based conditional mutual information estimation"
    - "a brief introduction to machine learning for engineers"
    - "universal estimation of directed information"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

Connected through hyperedge HE-4 (induced by source paper "idtxl the information dynamics toolkit xl a python package for the efficient analysis of multivariate information dynamics in networks"):
  with members:
    - "quantifying unique information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-5 (induced by source paper "control of criticality and computation in spiking neuromorphic networks with plasticity"):
  with members:
    - "demonstrating advantages of neuromorphic computation a pilot study"
    - "shared information new insights and problems in decomposing information in complex systems"

Connected through hyperedge HE-6 (induced by source paper "measuring sample path causal influences with relative entropy"):
  with members:
    - "on the bias of directed information estimators"
    - "a sample path measure of causal influence"
    - "consistent tomography under partial observations over adaptive networks"
    - "k nn estimation of directed information"
    - "estimating the directed information and testing for causality"
    - "strong completeness and faithfulness in bayesian networks"
    - "causal networks semantics and expressiveness"
    - "universal estimation of directed information"

Connected through hyperedge HE-7 (induced by source paper "a sample path measure of causal influence"):
  with members:
    - "universal estimation of directed information"
    - "directed information graphs"
    - "on directed information theory and granger causality graphs"

Connected through hyperedge HE-8 (induced by source paper "evaluation of preference of multimedia content using deep neural networks for electroencephalography"):
  with members:
    - "implicit analysis of perceptual multimedia experience based on physiological response a review"
    - "learning representations from eeg with deep recurrent convolutional neural networks"

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)