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: "reinforced co training"
  Abstract: Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled data and select samples based on prediction confidence to augment the training. However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space. In this paper, we propose a novel method, Reinforced Co-Training, to select high-quality unlabeled samples to better co-train on. More specifically, our approach uses Q-learning to learn a data selection policy with a small labeled dataset, and then exploits this policy to train the co-training classifiers automatically. Experimental results on clickbait detection and generic text classification tasks demonstrate that our proposed method can obtain more accurate text classification results.

Connected through hyperedge HE-1 (induced by source paper "a survey towards federated semi supervised learning"):
  with members:
    - "federated learning with matched averaging"
    - "advances and open problems in federated learning"
    - "adaptive image feature learning for disease classification using inductive graph networks"
    - "unsupervised data augmentation for consistency training"
    - "federated machine learning concept and applications"
    - "mixmatch a holistic approach to semi supervised learning"
    - "federated adversarial domain adaptation"
    - "secureboost a lossless federated learning framework"

Connected through hyperedge HE-2 (induced by source paper "optimizing data usage via differentiable rewards"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "self supervised generalisation with meta auxiliary learning"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "do better imagenet models transfer better"

Connected through hyperedge HE-3 (induced by source paper "dialog state tracking with reinforced data augmentation"):
  with members:
    - "improving dialogue state tracking by discerning the relevant context"
    - "deep communicating agents for abstractive summarization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "towards universal dialogue state tracking"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "banditsum extractive summarization as a contextual bandit"

Connected through hyperedge HE-4 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "learning how to active learn a deep reinforcement learning approach"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-5 (induced by source paper "selective transfer with reinforced transfer network for partial domain adaptation"):
  with members:
    - "homm higher order moment matching for unsupervised domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "learning to selectively transfer reinforced transfer learning for deep text matching"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "learning sampling policies for domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "learning to learn and predict a meta learning approach for multi label classification"):
  with members:
    - "deep reinforcement learning that matters"
    - "doctag2vec an embedding based multi label learning approach for document tagging"
    - "prototypical networks for few shot learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "neural architectures for fine grained entity type classification"

Connected through hyperedge HE-7 (induced by source paper "weak supervision for fake news detection via reinforcement learning"):
  with members:
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "reinforcement learning for relation classification from noisy data"
    - "fake news detection in social networks via crowd signals"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "automatic detection of fake news"
    - "fake news detection on social media a data mining perspective"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning based curriculum optimization for neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "automated curriculum learning for neural networks"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "neural machine translation training in a multi domain scenario"
    - "fast domain adaptation for neural machine translation"

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.CL(Computation and Language)