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: "automatic text scoring using neural networks"
  Abstract: Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the predictive features of the system need to be manually engineered by human experts. We introduce a model that forms word representations by learning the extent to which specific words contribute to the text's score. Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches. In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model has found more discriminative.

Connected through hyperedge HE-1 (induced by source paper "detection and mitigation of bias in ted talk ratings"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial robustness toolbox v1 0 0"
    - "on fairness and calibration"
    - "a survey on measuring indirect discrimination in machine learning"
    - "distributed representations of sentences and documents"
    - "fairness through awareness"

Connected through hyperedge HE-2 (induced by source paper "interpretable and steerable sequence learning via prototypes"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "ecg heartbeat classification a deep transferable representation"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "automatic rule extraction from long short term memory networks"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-3 (induced by source paper "latent adversarial defence with boundary guided generation"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "constructing unrestricted adversarial examples with generative models"
    - "label embedding with partial heterogeneous contexts"
    - "magnet a two pronged defense against adversarial examples"
    - "extending defensive distillation"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "learning from simulated and unsupervised images through adversarial training"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "language models and automated essay scoring"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "how to fine tune bert for text classification"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-5 (induced by source paper "a joint model for multimodal document quality assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "machine translation evaluation meets community question answering"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "deep paper gestalt"
    - "automatic academic paper rating based on modularized hierarchical convolutional neural network"
    - "skipflow incorporating neural coherence features for end to end automatic text scoring"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "fairyted a fair rating predictor for ted talk data"):
  with members:
    - "transfer of machine learning fairness across domains"
    - "a causality guided prediction of the ted talk ratings from the speech transcripts using neural networks"
    - "a survey on bias and fairness in machine learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "predictive inequity in object detection"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rawlsian fairness for machine learning"

Connected through hyperedge HE-7 (induced by source paper "predicting ted talk ratings from language and prosody"):
  with members:
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "fostering user engagement rhetorical devices for applause generation learnt from ted talks"
    - "convolutional neural network for humor recognition"
    - "regularizing and optimizing lstm language models"
    - "using argument based features to predict and analyse review helpfulness"
    - "globally normalized transition based neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "a causality guided prediction of the ted talk ratings from the speech transcripts using neural networks"):
  with members:
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "fostering user engagement rhetorical devices for applause generation learnt from ted talks"
    - "convolutional neural network for humor recognition"
    - "regularizing and optimizing lstm language models"
    - "using argument based features to predict and analyse review helpfulness"
    - "globally normalized transition based neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"

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)