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: "does object recognition work for everyone"
  Abstract: The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition. We find that the systems perform relatively poorly on household items that commonly occur in countries with a low household income. Qualitative analyses suggest the drop in performance is primarily due to appearance differences within an object class (e.g., dish soap) and due to items appearing in a different context (e.g., toothbrushes appearing outside of bathrooms). The results of our study suggest that further work is needed to make object-recognition systems work equally well for people across different countries and income levels.

Connected through hyperedge HE-1 (induced by source paper "don t judge an object by its context learning to overcome contextual bias"):
  with members:
    - "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"
    - "the elephant in the room"

Connected through hyperedge HE-2 (induced by source paper "towards fairer datasets filtering and balancing the distribution of the people subtree in the imagenet hierarchy"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "model cards for model reporting"
    - "implicit diversity in image summarization"
    - "large scale long tailed recognition in an open world"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-3 (induced by source paper "what is the point of fairness disability ai and the complexity of justice"):
  with members:
    - "implications of computer vision driven assistive technologies towards individuals with visual impairment"
    - "ai fairness for people with disabilities point of view"

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.CV(Computer Vision and Pattern Recognition)