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: "comparing measures of sparsity"
  Abstract: Sparsity of representations of signals has been shown to be a key concept of fundamental importance in fields such as blind source separation, compression, sampling and signal analysis. The aim of this paper is to compare several commonlyused sparsity measures based on intuitive attributes. Intuitively, a sparse representation is one in which a small number of coefficients contain a large proportion of the energy. In this paper six properties are discussed: (Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates and Babies), each of which a sparsity measure should have. The main contributions of this paper are the proofs and the associated summary table which classify commonly-used sparsity measures based on whether or not they satisfy these six propositions and the corresponding proofs. Only one of these measures satisfies all six: The Gini Index. measures based on whether or not they satisfy these six propositions and the corresponding proofs. Only one of these measures satisfies all six: The Gini Index.

Connected through hyperedge HE-1 (induced by source paper "concise explanations of neural networks using adversarial training"):
  with members:
    - "bridging adversarial robustness and gradient interpretability"
    - "on the robustness of interpretability methods"
    - "regularizing deep networks using efficient layerwise adversarial training"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "the limitations of deep learning in adversarial settings"

Connected through hyperedge HE-2 (induced by source paper "the variational infomax learning objective"):
  with members:
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"
    - "fixing a broken elbo"
    - "regularizing neural networks by penalizing confident output distributions"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"
    - "towards evaluating the robustness of neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "a discrete hard em approach for weakly supervised question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "learning to generalize from sparse and underspecified rewards"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "compositional questions do not necessitate multi hop reasoning"
    - "multi style generative reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "x sql reinforce schema representation with context"
    - "a bert baseline for the natural questions"

Connected through hyperedge HE-4 (induced by source paper "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "snip single shot network pruning based on connection sensitivity"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "structure learning for neural module networks"):
  with members:
    - "modular networks learning to decompose neural computation"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "evolving deep neural networks"
    - "modulating early visual processing by language"

Connected through hyperedge HE-6 (induced by source paper "grouped sparse projection"):
  with members:
    - "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"
    - "learning sparse networks using targeted dropout"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "importance estimation for neural network pruning"
    - "the state of sparsity in deep neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "inductive bias of deep convolutional networks through pooling geometry"

Connected through hyperedge HE-7 (induced by source paper "recovery of future data via convolution nuclear norm minimization"):
  with members:
    - "matrix completion with deterministic sampling theories and methods"
    - "gain missing data imputation using generative adversarial nets"
    - "matrix completion has no spurious local minimum"
    - "recurrent neural networks for multivariate time series with missing values"
    - "incoherence optimal matrix completion"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "robust recovery of subspace structures by low rank representation"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-8 (induced by source paper "minimal sample subspace learning theory and algorithms"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "innovation pursuit a new approach to subspace clustering"
    - "robust subspace clustering via smoothed rank approximation"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"

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)