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: "efficient estimation of mutual information for strongly dependent variables"
  Abstract: We demonstrate that a popular class of nonparametric mutual information (MI) estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI. Consequently, accurate estimation of MI between two strongly dependent variables is possible only for prohibitively large sample size. This important yet overlooked shortcoming of the existing estimators is due to their implicit reliance on local uniformity of the underlying joint distribution. We introduce a new estimator that is robust to local non-uniformity, works well with limited data, and is able to capture relationship strengths over many orders of magnitude. We demonstrate the superior performance of the proposed estimator on both synthetic and real-world data.

Connected through hyperedge HE-1 (induced by source paper "mint deep network compression via mutual information based neuron trimming"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "geometric estimation of multivariate dependency"
    - "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "model augmented estimation of conditional mutual information for feature selection"):
  with members:
    - "on variational bounds of mutual information"
    - "ensemble estimation of mutual information"
    - "estimating mutual information for discrete continuous mixtures"
    - "demystifying fixed k nearest neighbor information estimators"

Connected through hyperedge HE-3 (induced by source paper "on variational bounds of mutual information"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "invariant representations without adversarial training"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "formal limitations on the measurement of mutual information"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (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"
    - "ccmi classifier based conditional mutual information estimation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "distributed generation of privacy preserving data with user customization"):
  with members:
    - "understanding compressive adversarial privacy"
    - "protecting sensitive attributes via generative adversarial networks"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "learning to protect communications with adversarial neural cryptography"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-6 (induced by source paper "learning the arrow of time"):
  with members:
    - "time reversal as self supervision"
    - "measuring and avoiding side effects using relative reachability"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "episodic curiosity through reachability"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "ai safety gridworlds"
    - "low impact artificial intelligences"

Connected through hyperedge HE-7 (induced by source paper "geometric estimation of multivariate dependency"):
  with members:
    - "a dimension independent discriminant between distributions"
    - "convergence rates for empirical estimation of binary classification bounds"
    - "scalable mutual information estimation using dependence graphs"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of mutual information"
    - "improving convergence of divergence functional ensemble estimators"
    - "a review of feature selection methods based on mutual information"

Connected through hyperedge HE-8 (induced by source paper "deep learning for channel coding via neural mutual information estimation"):
  with members:
    - "deep reinforcement learning autoencoder with noisy feedback"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "mine mutual information neural estimation"
    - "an introduction to deep learning for the physical layer"
    - "demystifying fixed k nearest neighbor information estimators"
    - "adam a method for stochastic optimization"
    - "tighter variational representations of f divergences via restriction to probability measures"

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)