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: "bias in bios a case study of semantic representation bias in a high stakes setting"
  Abstract: We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the potential allocation harms that can result from semantic representation bias. To do so, we study the impact on occupation classification of including explicit gender indicators---such as first names and pronouns---in different semantic representations of online biographies. Additionally, we quantify the bias that remains when these indicators are "scrubbed," and describe proxy behavior that occurs in the absence of explicit gender indicators. As we demonstrate, differences in true positive rates between genders are correlated with existing gender imbalances in occupations, which may compound these imbalances.

Connected through hyperedge HE-1 (induced by source paper "chexclusion fairness gaps in deep chest x ray classifiers"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "a simulation based dynamic evaluation framework for system wide algorithmic fairness"
    - "on the fairness of disentangled representations"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-2 (induced by source paper "an attentive survey of attention models"):
  with members:
    - "targeted sentiment classification with attentional encoder network"
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention models in graphs a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "self attentive sequential recommendation"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "bias remediation in driver drowsiness detection systems using generative adversarial networks"):
  with members:
    - "racial categories in machine learning"
    - "detecting inter sectional accuracy differences in driver drowsiness detection algorithms"
    - "data augmentation for low resource sentiment analysis using generative adversarial networks"
    - "depthwisegans fast training generative adversarial networks for realistic image synthesis"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "text to image synthesis using generative adversarial networks"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "domain adaptive transfer learning with specialist models"

Connected through hyperedge HE-4 (induced by source paper "fairness in representation quantifying stereotyping as a representational harm"):
  with members:
    - "fair algorithms for clustering"
    - "understanding the origins of bias in word embeddings"
    - "convex formulations for fair principal component analysis"
    - "fair clustering through fairlets"
    - "the price of fair pca one extra dimension"
    - "learning adversarially fair and transferable representations"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-5 (induced by source paper "the disparate equilibria of algorithmic decision making when individuals invest rationally"):
  with members:
    - "optimal decision making under strategic behavior"
    - "long term impact of fair machine learning in sequential decision making representation disparity and group retention"
    - "the disparate effects of strategic manipulation"
    - "delayed impact of fair machine learning"
    - "how do classifiers induce agents to invest effort strategically"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"

Connected through hyperedge HE-6 (induced by source paper "learning to deceive with attention based explanations"):
  with members:
    - "attention is not not explanation"
    - "on identifiability in transformers"
    - "on the validity of self attention as explanation in transformer models"
    - "attention is not explanation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-7 (induced by source paper "esa entity summarization with attention"):
  with members:
    - "an attentive survey of attention models"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "what you see is what you get the impact of representation criteria on human bias in hiring"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "discrimination in the age of algorithms"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "ranking with fairness constraints"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

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.IR(Information Retrieval)