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: "avoiding your teacher s mistakes training neural networks with controlled weak supervision"
  Abstract: Training deep neural networks requires massive amounts of training data, but for many tasks only limited labeled data is available. This makes weak supervision attractive, using weak or noisy signals like the output of heuristic methods or user click-through data for training. In a semi-supervised setting, we can use a large set of data with weak labels to pretrain a neural network and then fine-tune the parameters with a small amount of data with true labels. This feels intuitively sub-optimal as these two independent stages leave the model unaware about the varying label quality. What if we could somehow inform the model about the label quality? In this paper, we propose a semi-supervised learning method where we train two neural networks in a multi-task fashion: a "target network" and a "confidence network". The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to weight the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model. We evaluate our learning strategy on two different tasks: document ranking and sentiment classification. The results demonstrate that our approach not only enhances the performance compared to the baselines but also speeds up the learning process from weak labels.

Connected through hyperedge HE-1 (induced by source paper "suppressing uncertainties for large scale facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "noise tolerant paradigm for training face recognition cnns"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "arcface additive angular margin loss for deep face recognition"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-2 (induced by source paper "learning not to learn in the presence of noisy labels"):
  with members:
    - "laprop a better way to combine momentum with adaptive gradient"
    - "deep gamblers learning to abstain with portfolio theory"
    - "sgd on neural networks learns functions of increasing complexity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "how does disagreement help generalization against label corruption"
    - "selectivenet a deep neural network with an integrated reject option"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"

Connected through hyperedge HE-3 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-4 (induced by source paper "task driven data verification via gradient descent"):
  with members:
    - "learning to reweight examples for robust deep learning"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"
    - "understanding deep learning requires rethinking generalization"
    - "scalable gradient based tuning of continuous regularization hyperparameters"
    - "training convolutional networks with noisy labels"
    - "identifying mislabeled training data"

Connected through hyperedge HE-5 (induced by source paper "neural networks for information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "attentive memory networks efficient machine reading for conversational search"
    - "neural ranking models with weak supervision"
    - "reply with proactive recommendation of email attachments"
    - "a neural knowledge language model"
    - "learning to attend copy and generate for session based query suggestion"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-6 (induced by source paper "learning matching models with weak supervision for response selection in retrieval based chatbots"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "hierarchical recurrent attention network for response generation"
    - "fidelity weighted learning"
    - "topic aware neural response generation"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "syntax based deep matching of short texts"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-7 (induced by source paper "learning to rank from samples of variable quality"):
  with members:
    - "neural ranking models with weak supervision"
    - "learning to attend copy and generate for session based query suggestion"
    - "efficient and effective spam filtering and re ranking for large web datasets"

Connected through hyperedge HE-8 (induced by source paper "fidelity weighted learning"):
  with members:
    - "neural ranking models with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "decoupling when to update from how to update"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning to attend copy and generate for session based query suggestion"
    - "loss factorization weakly supervised learning and label noise robustness"
    - "socratic learning augmenting generative models to incorporate latent subsets in training data"
    - "adversarial feature learning"

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)