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: "ccmi classifier based conditional mutual information estimation"
  Abstract: Conditional Mutual Information (CMI) is a measure of conditional dependence between random variables X and Y, given another random variable Z. It can be used to quantify conditional dependence among variables in many data-driven inference problems such as graphical models, causal learning, feature selection and time-series analysis. While k-nearest neighbor (kNN) based estimators as well as kernel-based methods have been widely used for CMI estimation, they suffer severely from the curse of dimensionality. In this paper, we leverage advances in classifiers and generative models to design methods for CMI estimation. Specifically, we introduce an estimator for KL-Divergence based on the likelihood ratio by training a classifier to distinguish the observed joint distribution from the product distribution. We then show how to construct several CMI estimators using this basic divergence estimator by drawing ideas from conditional generative models. We demonstrate that the estimates from our proposed approaches do not degrade in performance with increasing dimension and obtain significant improvement over the widely used KSG estimator. Finally, as an application of accurate CMI estimation, we use our best estimator for conditional independence testing and achieve superior performance than the state-of-the-art tester on both simulated and real data-sets.

Connected through hyperedge HE-1 (induced by source paper "conditional mutual information based contrastive loss for financial time series forecasting"):
  with members:
    - "on mutual information maximization for representation learning"
    - "understanding the limitations of variational mutual information estimators"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "attention is all you need"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "understanding the limitations of variational mutual information estimators"):
  with members:
    - "on mutual information maximization for representation learning"
    - "on variational bounds of mutual information"
    - "wasserstein dependency measure for representation learning"
    - "formal limitations on the measurement of mutual information"
    - "towards a definition of disentangled representations"
    - "mine mutual information neural estimation"
    - "bias and generalization in deep generative models an empirical study"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "conditional mutual information neural estimator"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "entropy and mutual information in models of deep neural networks"
    - "formal limitations on the measurement of mutual information"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "deep learning for the gaussian wiretap channel"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-4 (induced by source paper "itene intrinsic transfer entropy neural estimator"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "a brief introduction to machine learning for engineers"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "universal estimation of directed information"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "softmax is not an artificial trick an information theoretic view of softmax in neural networks"):
  with members:
    - "on variational bounds of mutual information"
    - "mine mutual information neural estimation"
    - "learning deep features for discriminative localization"

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.LG(Machine Learning)