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: "demographic dialectal variation in social media a case study of african american english"
  Abstract: Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language. We conduct a case study of dialectal language in online conversational text by investigating African-American English (AAE) on Twitter. We propose a distantly supervised model to identify AAE-like language from demographics associated with geo-located messages, and we verify that this language follows well-known AAE linguistic phenomena. In addition, we analyze the quality of existing language identification and dependency parsing tools on AAE-like text, demonstrating that they perform poorly on such text compared to text associated with white speakers. We also provide an ensemble classifier for language identification which eliminates this disparity and release a new corpus of tweets containing AAE-like language.

Connected through hyperedge HE-1 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "measurement and fairness"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "theory in theory out the uses of social theory in machine learning for social science"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "an intersectional definition of fairness"
    - "automated hate speech detection and the problem of offensive language"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "fairness in deep learning a computational perspective"):
  with members:
    - "learning credible deep neural networks with rationale regularization"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "incorporating priors with feature attribution on text classification"
    - "deep learning for face recognition pride or prejudiced"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "compositional fairness constraints for graph embeddings"
    - "flexibly fair representation learning by disentanglement"

Connected through hyperedge HE-4 (induced by source paper "differential privacy has disparate impact on model accuracy"):
  with members:
    - "diversity in faces"
    - "fair decision making using privacy protected data"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "class balanced loss based on effective number of samples"
    - "agnostic federated learning"
    - "federated heavy hitters discovery with differential privacy"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "adversarial removal of demographic attributes from text data"

Connected through hyperedge HE-5 (induced by source paper "distributionally robust neural networks for group shifts on the importance of regularization for worst case generalization"):
  with members:
    - "invariance inducing regularization using worst case transformations suffices to boost accuracy and spatial robustness"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "distributionally robust language modeling"
    - "class balanced loss based on effective number of samples"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "racial bias in hate speech and abusive language detection datasets"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "reducing gender bias in abusive language detection"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "racial disparity in natural language processing a case study of social media african american english"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-7 (induced by source paper "correcting sociodemographic selection biases for accurate population prediction from social media"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "controlling for unobserved confounds in classification using correlational constraints"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "confounds and consequences in geotagged twitter data"

Connected through hyperedge HE-8 (induced by source paper "long term impact of fair machine learning in sequential decision making representation disparity and group retention"):
  with members:
    - "delayed impact of fair machine learning"
    - "the frontiers of fairness in machine learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "inherent trade offs in the fair determination of risk scores"

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)