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: "scalable mutual information estimation using dependence graphs"
  Abstract: We propose a unified method for empirical non-parametric estimation of general Mutual Information (MI) function between the random vectors in $\mathbb{R}^d$ based on $N$ i.i.d. samples. The proposed low complexity estimator is based on a bipartite graph, referred to as dependence graph. The data points are mapped to the vertices of this graph using randomized Locality Sensitive Hashing (LSH). The vertex and edge weights are defined in terms of marginal and joint hash collisions. For a given set of hash parameters $\epsilon(1), \ldots, \epsilon(k)$, a base estimator is defined as a weighted average of the transformed edge weights. The proposed estimator, called the ensemble dependency graph estimator (EDGE), is obtained as a weighted average of the base estimators, where the weights are computed offline as the solution of a linear programming problem. EDGE achieves optimal computational complexity $O(N)$, and can achieve the optimal parametric MSE rate of $O(1/N)$ if the density is $d$ times differentiable. To the best of our knowledge EDGE is the first non-parametric MI estimator that can achieve parametric MSE rates with linear time complexity.

Connected through hyperedge HE-1 (induced by source paper "to tune or not to tune adapting pretrained representations to diverse tasks"):
  with members:
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-2 (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"
    - "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"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-3 (induced by source paper "information theoretic perspective of federated learning"):
  with members:
    - "efficient decentralized deep learning by dynamic model averaging"
    - "the loss surface of deep and wide neural networks"
    - "opening the black box of deep neural networks via information"
    - "communication efficient learning of deep networks from decentralized data"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-4 (induced by source paper "evaluation of dataflow through layers of deep neural networks in classification and regression problems"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "opening the black box of deep neural networks via information"
    - "on loss functions for deep neural networks in classification"

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)