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: "stereotyping and bias in the flickr30k dataset"
  Abstract: An untested assumption behind the crowdsourced descriptions of the images in the Flickr30K dataset (Young et al., 2014) is that they "focus only on the information that can be obtained from the image alone" (Hodosh et al., 2013, p. 859). This paper presents some evidence against this assumption, and provides a list of biases and unwarranted inferences that can be found in the Flickr30K dataset. Finally, it considers methods to find examples of these, and discusses how we should deal with stereotype-driven descriptions in future applications.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"

Connected through hyperedge HE-2 (induced by source paper "don t judge an object by its context learning to overcome contextual bias"):
  with members:
    - "does object recognition work for everyone"
    - "mitigating gender bias in natural language processing literature review"
    - "repair removing representation bias by dataset resampling"
    - "class balanced loss based on effective number of samples"
    - "laso label set operations networks for multi label few shot learning"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "learning not to learn training deep neural networks with biased data"

Connected through hyperedge HE-3 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models"):
  with members:
    - "right for the right reason training agnostic networks"
    - "explicit bias discovery in visual question answering models"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "learning gender neutral word embeddings"

Connected through hyperedge HE-4 (induced by source paper "adversarial regularization for visual question answering strengths shortcomings and side effects"):
  with members:
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "learning to count objects in natural images for visual question answering"
    - "hypothesis only baselines in natural language inference"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "overcoming language priors in visual question answering with adversarial regularization"

Connected through hyperedge HE-5 (induced by source paper "predicting demographics moral foundations and human values from digital behaviours"):
  with members:
    - "studying migrant assimilation through facebook interests"
    - "acquiring background knowledge to improve moral value prediction"
    - "how well can machine learning predict demographics of social media users"
    - "you are what apps you use demographic prediction based on user s apps"
    - "a survey on measuring indirect discrimination in machine learning"
    - "using deep learning to predict demographics from mobile phone metadata"
    - "fairness through awareness"

Connected through hyperedge HE-6 (induced by source paper "webly supervised joint embedding for cross modal image text retrieval"):
  with members:
    - "exploring stereotypes and biased data with the crowd"
    - "sampling matters in deep embedding learning"
    - "learning two branch neural networks for image text matching tasks"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "dual path convolutional image text embedding with instance loss"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning social image embedding with deep multimodal attention networks"

Connected through hyperedge HE-7 (induced by source paper "women also snowboard overcoming bias in captioning models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mitigating unwanted biases with adversarial learning"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "inclusivefacenet improving face attribute detection with race and gender diversity"

Connected through hyperedge HE-8 (induced by source paper "word embeddings quantify 100 years of gender and ethnic stereotypes"):
  with members:
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "structured embedding models for grouped data"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "statistically significant detection of linguistic change"
    - "efficient estimation of word representations in vector space"

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)