human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards fairer datasets filtering and balancing the distribution of the people subtree in the imagenet hierarchy"):
  Abstract: Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the "person" subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively.

Includes members:
  Member "50 years of test un fairness lessons for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fairness in deep learning a computational perspective")
  Member "does object recognition work for everyone":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t judge an object by its context learning to overcome contextual bias")
  Member "model cards for model reporting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "implicit diversity in image summarization":
    also connected through other hyperedges:
      (none)
  Member "large scale long tailed recognition in an open world":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incremental few shot object detection")
  Member "women also snowboard overcoming bias in captioning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unbiased scene graph generation from biased training")
  Member "mitigating unwanted biases with adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithmic fairness")

Question: Please tell me which class the source paper 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)