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: "large scale multi label learning with missing labels"
  Abstract: The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number (say millions) of labels, and (b) the ability to handle data with missing labels. In this paper, we directly address both these problems by studying the multi-label problem in a generic empirical risk minimization (ERM) framework. Our framework, despite being simple, is surprisingly able to encompass several recent label-compression based methods which can be derived as special cases of our method. To optimize the ERM problem, we develop techniques that exploit the structure of specific loss functions - such as the squared loss function - to offer efficient algorithms. We further show that our learning framework admits formal excess risk bounds even in the presence of missing labels. Our risk bounds are tight and demonstrate better generalization performance for low-rank promoting trace-norm regularization when compared to (rank insensitive) Frobenius norm regularization. Finally, we present extensive empirical results on a variety of benchmark datasets and show that our methods perform significantly better than existing label compression based methods and can scale up to very large datasets such as the Wikipedia dataset.

Connected through hyperedge HE-1 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "efficient large scale domain classification with personalized attention"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "classification from positive unlabeled and biased negative data"
    - "supervised domain enablement attention for personalized domain classification"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-2 (induced by source paper "eev dataset predicting expressions evoked by diverse videos"):
  with members:
    - "gla in mediaeval 2018 emotional impact of movies task"
    - "a compact embedding for facial expression similarity"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learnable pooling with context gating for video classification"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "joint ranking svm and binary relevance with robust low rank learning for multi label classification"):
  with members:
    - "multi label learning with global and local label correlation"
    - "a unified view of multi label performance measures"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-5 (induced by source paper "tensor decomposition with relational constraints for predicting multiple types of microrna disease associations"):
  with members:
    - "similarity learning via kernel preserving embedding"

Connected through hyperedge HE-6 (induced by source paper "structured prediction with partial labelling through the infimum loss"):
  with members:
    - "a consistent regularization approach for structured prediction"

Connected through hyperedge HE-7 (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-8 (induced by source paper "learning a deep convnet for multi label classification with partial labels"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "webvision challenge visual learning and understanding with web 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)