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: "structured sparsity through convex optimization"
  Abstract: Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or feature selection admits a convex relaxation through the regularization by the $\ell_1$-norm. In this paper, we consider situations where we are not only interested in sparsity, but where some structural prior knowledge is available as well. We show that the $\ell_1$-norm can then be extended to structured norms built on either disjoint or overlapping groups of variables, leading to a flexible framework that can deal with various structures. We present applications to unsupervised learning, for structured sparse principal component analysis and hierarchical dictionary learning, and to supervised learning in the context of non-linear variable selection.

Connected through hyperedge HE-1 (induced by source paper "joint group feature selection and discriminative filter learning for robust visual object tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "deep visual attention prediction"
    - "distractor aware siamese networks for visual object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "high speed tracking with multi kernel correlation filters"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"
    - "robust visual tracking revisited from correlation filter to template matching"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-3 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "how to escape saddle points efficiently"
    - "a survey on social media anomaly detection"
    - "unregularized online learning algorithms with general loss functions"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"
    - "on iterative hard thresholding methods for high dimensional m estimation"

Connected through hyperedge HE-4 (induced by source paper "image smoothing via unsupervised learning"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "deep image prior"
    - "semi global weighted least squares in image filtering"
    - "deep texture and structure aware filtering network for image smoothing"
    - "deep bilateral learning for real time image enhancement"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-5 (induced by source paper "feature grouping as a stochastic regularizer for high dimensional structured data"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "playing atari with deep reinforcement learning"
    - "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "the numpy array a structure for efficient numerical computation"
    - "adding noise to the input of a model trained with a regularized objective"

Connected through hyperedge HE-6 (induced by source paper "using feature grouping as a stochastic regularizer for high dimensional noisy data"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "playing atari with deep reinforcement learning"
    - "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "adding noise to the input of a model trained with a regularized objective"

Connected through hyperedge HE-7 (induced by source paper "subic a supervised structured binary code for image search"):
  with members:
    - "billion scale similarity search with gpus"
    - "deep image retrieval learning global representations for image search"
    - "approximate search with quantized sparse representations"
    - "how should we evaluate supervised hashing"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-8 (induced by source paper "complex structure leads to overfitting a structure regularization decoding method for natural language processing"):
  with members:
    - "f score driven max margin neural network for named entity recognition in chinese social media"
    - "multilingual language processing from bytes"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "adam a method for stochastic optimization"
    - "structure regularization for structured prediction theories and experiments"
    - "efficient decomposed learning for structured prediction"
    - "natural language processing almost from scratch"

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.LG(Machine Learning)