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 "algorithmic bias and the biases of the bias catchers"):
  Abstract: Concerns about gender bias have captured most of the attention in the AI research literature on the topic of bias in word embeddings models. In this work, a systematic analysis of popular word embedding models shows that many of those concerns are probably exaggerated. Gender bias in these models is often mild and frequently reversed in polarity to what has been regularly reported. Interestingly, other types of so far unreported moderate biases in word embedding models have been identified. Specifically, biases against intellectual phenomena such as political and religious orientation. The mismatch between the results reported here and the dominant themes in the relevant literature might be due to another type of bias entrenched within the algorithmic fairness epistemic community and that manifests itself as a predilection for exploring primordially only certain regions of the research landscape.

Includes members:
  Member "the geometry of culture analyzing the meanings of class through word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural embeddings of scholarly periodicals reveal complex disciplinary organizations")
  Member "from word to sense embeddings a survey on vector representations of meaning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tedl a text encryption method based on deep learning")
  Member "word embeddings quantify 100 years of gender and ethnic stereotypes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "semantics derived automatically from language corpora contain human like biases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "think locally act globally federated learning with local and global representations")

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.CY(Computers and Society)