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 "neural embeddings of scholarly periodicals reveal complex disciplinary organizations"):
  Abstract: Understanding the structure of knowledge domains has been one of the foundational challenges in the science of science. Although there have been a series of studies on the self-organized structure of knowledge domains and their relationships, creating rich, coherent, and quantitative representation frameworks is still an open challenge. Meanwhile, neural embedding methods, which learn continuous vector representations of entities by using neural networks and contextual information, are emerging as a powerful representation framework that can encode nuanced semantic relationships into geometric ones. Here, we propose a neural embedding technique that leverages the information contained in the paper citation network to obtain continuous representations of scientific periodicals. We demonstrate that our embeddings encode nuanced relationships between periodicals as well as the complex disciplinary structure of science, even allowing us to make cross-disciplinary analogies between periodicals. Furthermore, we show that the embeddings capture meaningful "axes" that encompass all disciplines in the knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground a periodical on a given spectrum. Using this new capacity, we test the hypothesis of the hierarchy of the sciences, showing that, in most disciplines such as Social Sciences and Life Sciences, most widely cited papers tend to appear in "harder" periodicals. Our framework may offer novel quantification methods in science of science, which may in turn facilitate the study of how knowledge is created and organized.

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 "wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types")
  Member "the preeminence of ethnic diversity in scientific collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "china may need to support more small teams in scientific research")
  Member "semaxis a lightweight framework to characterize domain specific word semantics beyond sentiment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the polar framework polar opposites enable interpretability of pre trained word embeddings")
  Member "a style based generator architecture for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "word embeddings quantify 100 years of gender and ethnic stereotypes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep graph matching consensus")
  Member "diachronic word embeddings reveal statistical laws of semantic change":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a framework for the computational linguistic analysis of dehumanization")

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.DL(Digital Libraries)