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: "estimators for multivariate information measures in general probability spaces"
  Abstract: Information theoretic quantities play an important role in various settings in machine learning, including causality testing, structure inference in graphical models, time-series problems, feature selection as well as in providing privacy guarantees. A key quantity of interest is the mutual information and generalizations thereof, including conditional mutual information, multivariate mutual information, total correlation and directed information. While the aforementioned information quantities are well defined in arbitrary probability spaces, existing estimators add or subtract entropies (we term them $\Sigma H$ methods). These methods work only in purely discrete space or purely continuous case since entropy (or differential entropy) is well defined only in that regime. #R##N#In this paper, we define a general graph divergence measure ($\mathbb{GDM}$), as a measure of incompatibility between the observed distribution and a given graphical model structure. This generalizes the aforementioned information measures and we construct a novel estimator via a coupling trick that directly estimates these multivariate information measures using the Radon-Nikodym derivative. These estimators are proven to be consistent in a general setting which includes several cases where the existing estimators fail, thus providing the only known estimators for the following settings: (1) the data has some discrete and some continuous-valued components (2) some (or all) of the components themselves are discrete-continuous mixtures (3) the data is real-valued but does not have a joint density on the entire space, rather is supported on a low-dimensional manifold. We show that our proposed estimators significantly outperform known estimators on synthetic and real datasets.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis of image registration"):
  with members:
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "a deep metric for multimodal registration"
    - "fundamental limits in multi image alignment"
    - "demystifying fixed k nearest neighbor information estimators"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "channels with cost constraints strong converse and dispersion"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "ccmi classifier based conditional mutual information estimation"):
  with members:
    - "on calibration of modern neural networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "breaking the bandwidth barrier geometrical adaptive entropy estimation"
    - "demystifying fixed k nearest neighbor information estimators"
    - "conditional generative adversarial nets"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "learning module networks"
    - "obtaining calibrated probabilities from boosting"

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)