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: "training classifiers with natural language explanations"
  Abstract: Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100$\times$ faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.

Connected through hyperedge HE-1 (induced by source paper "generative grading neural approximate parsing for automated student feedback"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reliable deep grade prediction with uncertainty estimation"
    - "automated essay scoring based on two stage learning"
    - "multimodal generative models for scalable weakly supervised learning"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "attention is all you need"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "inference compilation and universal probabilistic programming"

Connected through hyperedge HE-2 (induced by source paper "machine teaching by domain experts towards more humane inclusive and intelligent machine learning systems"):
  with members:
    - "human level intelligence or animal like abilities"
    - "machine teaching a new paradigm for building machine learning systems"

Connected through hyperedge HE-3 (induced by source paper "deep generative models with learnable knowledge constraints"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "learning to write with cooperative discriminators"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward controlled generation of text"
    - "equivalence between policy gradients and soft q learning"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"

Connected through hyperedge HE-4 (induced by source paper "snorkel rapid training data creation with weak supervision"):
  with members:
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "fonduer knowledge base construction from richly formatted data"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "inferring generative model structure with static analysis"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning the structure of generative models without labeled data"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "tensorflow a system for large scale machine 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.CL(Computation and Language)