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: "multi label prediction via compressed sensing"
  Abstract: We consider multi-label prediction problems with large output spaces under the assumption of output sparsity -- that the target (label) vectors have small support. We develop a general theory for a variant of the popular error correcting output code scheme, using ideas from compressed sensing for exploiting this sparsity. The method can be regarded as a simple reduction from multi-label regression problems to binary regression problems. We show that the number of subproblems need only be logarithmic in the total number of possible labels, making this approach radically more efficient than others. We also state and prove robustness guarantees for this method in the form of regret transform bounds (in general), and also provide a more detailed analysis for the linear prediction setting.

Connected through hyperedge HE-1 (induced by source paper "crowd scene analysis by output encoding"):
  with members:
    - "point in box out beyond counting persons in crowds"
    - "centernet keypoint triplets for object detection"
    - "selective kernel networks"
    - "scale aware trident networks for object detection"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "where are the blobs counting by localization with point supervision"
    - "top down feedback for crowd counting convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "extreme regression for dynamic search advertising"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "attentionxml label tree based attention aware deep model for high performance extreme multi label text classification"
    - "fast label embeddings for extremely large output spaces"
    - "lshtc a benchmark for large scale text classification"
    - "a theoretical analysis of ndcg type ranking measures"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-3 (induced by source paper "word class embeddings for multiclass text classification"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "enhancing domain word embedding via latent semantic imputation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep learning for sentiment analysis a survey"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"

Connected through hyperedge HE-4 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "zero shot user intent detection via capsule neural networks"
    - "towards linear time neural machine translation with capsule networks"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "dynamic routing between capsules"
    - "attentive pooling networks"

Connected through hyperedge HE-5 (induced by source paper "label aware document representation via hybrid attention for extreme multi label text classification"):
  with members:
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "sgm sequence generation model for multi label classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explicit interaction model towards text classification"
    - "deep extreme multi label learning"
    - "a structured self attentive sentence embedding"
    - "node2vec scalable feature learning for networks"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"

Connected through hyperedge HE-6 (induced by source paper "mute data similarity driven multi hot target encoding for neural network design"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "beyond one hot encoding lower dimensional target embedding"
    - "datasheets for datasets"
    - "on the limitation of convolutional neural networks in recognizing negative images"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "label embedding for image classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "extreme classification in log memory using count min sketch a case study of amazon search with 50m products"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "mesh tensorflow deep learning for supercomputers"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "logarithmic time online multiclass prediction"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "collaborative filtering and multi label classification with matrix factorization"):
  with members:
    - "cnn rnn a unified framework for multi label image classification"
    - "robust low rank matrix factorization under general mixture noise distributions"
    - "scalable multilabel prediction via randomized methods"
    - "large scale multi label learning with missing labels"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "the landmark selection method for multiple output prediction"
    - "a tutorial on spectral clustering"
    - "non negative matrix factorization with sparseness constraints"

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)