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: "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"
  Abstract: It is one typical and general topic of learning a good embedding model to efficiently learn the representation coefficients between two spaces/subspaces. To solve this task, $L_{1}$ regularization is widely used for the pursuit of feature selection and avoiding overfitting, and yet the sparse estimation of features in $L_{1}$ regularization may cause the underfitting of training data. $L_{2}$ regularization is also frequently used, but it is a biased estimator. In this paper, we propose the idea that the features consist of three orthogonal parts, \emph{namely} sparse strong signals, dense weak signals and random noise, in which both strong and weak signals contribute to the fitting of data. To facilitate such novel decomposition, \emph{MSplit} LBI is for the first time proposed to realize feature selection and dense estimation simultaneously. We provide theoretical and simulational verification that our method exceeds $L_{1}$ and $L_{2}$ regularization, and extensive experimental results show that our method achieves state-of-the-art performance in the few-shot and zero-shot learning.

Connected through hyperedge HE-1 (induced by source paper "a plug in method for representation factorization"):
  with members:
    - "seeing what a gan cannot generate"
    - "wasserstein dependency measure for representation learning"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "generative adversarial networks gans what it can generate and what it cannot"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural voice cloning with a few samples"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "class regularization improve few shot image classification by reducing meta shift"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "generalizing from a few examples a survey on few shot learning"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-3 (induced by source paper "hierarchical prototype learning for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "discriminative learning of latent features for zero shot recognition"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "parsimonious deep learning a differential inclusion approach with global convergence"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "gradient descent finds global minima of deep neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "transfer learning with neural automl"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning for few shot image classification"):
  with members:
    - "contrastive multiview coding"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "a closer look at few shot classification"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "isplit lbi individualized partial ranking with ties via split lbi"):
  with members:
    - "a margin based mle for crowdsourced partial ranking"
    - "from social to individuals a parsimonious path of multi level models for crowdsourced preference aggregation"
    - "adversarial personalized ranking for recommendation"
    - "parsimonious mixed effects hodgerank for crowdsourced preference aggregation"

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.CV(Computer Vision and Pattern Recognition)