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: "towards ultrahigh dimensional feature selection for big data"
  Abstract: In this paper, we present a new adaptive feature scaling scheme for ultrahigh-dimensional feature selection on Big Data. To solve this problem effectively, we first reformulate it as a convex semi-infinite programming (SIP) problem and then propose an efficient \emph{feature generating paradigm}. In contrast with traditional gradient-based approaches that conduct optimization on all input features, the proposed method iteratively activates a group of features and solves a sequence of multiple kernel learning (MKL) subproblems of much reduced scale. To further speed up the training, we propose to solve the MKL subproblems in their primal forms through a modified accelerated proximal gradient approach. Due to such an optimization scheme, some efficient cache techniques are also developed. The feature generating paradigm can guarantee that the solution converges globally under mild conditions and achieve lower feature selection bias. Moreover, the proposed method can tackle two challenging tasks in feature selection: 1) group-based feature selection with complex structures and 2) nonlinear feature selection with explicit feature mappings. Comprehensive experiments on a wide range of synthetic and real-world datasets containing tens of million data points with $O(10^{14})$ features demonstrate the competitive performance of the proposed method over state-of-the-art feature selection methods in terms of generalization performance and training efficiency.

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (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-3 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "solving graph compression via optimal transport"):
  with members:
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "learning small predictors"
    - "how powerful are graph neural networks"
    - "spectrally approximating large graphs with smaller graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "continuous flow graph transportation distances"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-5 (induced by source paper "mptv matching pursuit based total variation minimization for image deconvolution"):
  with members:
    - "learning an optimizer for image deconvolution"
    - "group based sparse representation for image restoration"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-6 (induced by source paper "discrimination aware channel pruning for deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "towards effective low bitwidth convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "mission ultra large scale feature selection using count sketches"):
  with members:
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "efficient estimation of word representations in vector space"
    - "a reliable effective terascale linear learning system"
    - "coherence analysis of iterative thresholding algorithms"
    - "feature hashing for large scale multitask learning"
    - "sparse online learning via truncated gradient"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "a tutorial on distance metric learning mathematical foundations algorithms and experiments"):
  with members:
    - "a snapshot on nonstandard supervised learning problems taxonomy relationships problem transformations and algorithm adaptations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "a survey on metric learning for feature vectors and structured data"

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)