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 "co evolution of language and agents in referential games"):
  Abstract: Referential games offer a grounded learning environment for neural agents, that accounts for the functional aspects of language. However, they fail to account for another fundamental aspect of human language: Because languages are transmitted from generation to generation, they have to be learnable by new language users, which makes them subject to cultural evolution. Recent work has shown that incorporating cultural evolution in referential game results in considerable improvements in the properties of the languages that emerge in the game. In this work, we first substantiate this claim with a different data set and a wider array of evaluation metrics. Then, drawing inspiration from linguistic theories of human language evolution, we consider a scenario in which not only cultural but also genetic evolution is integrated. As our core contribution, we introduce the Language Transmission Engine, in which cultural evolution of the language is combined with genetic evolution of the agents' architecture. We show that this co-evolution scenario leads to across-the-board improvements on all considered metrics. These results stress that cultural evolution is important for language emergence studies, but also the suitability of the architecture itself should be considered.

Includes members:
  Member "random search and reproducibility for neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "miss tools and mr fruit emergent communication in agents learning about object affordances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the possibility of rewarding structure learning agents mutual information on linguistic random sets")
  Member "emergence of compositional language with deep generational transmission":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "compositional obverter communication learning from raw visual input":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "emergent communication with world models")
  Member "how agents see things on visual representations in an emergent language game":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards graph representation learning in emergent communication")
  Member "darts differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "emergence of linguistic communication from referential games with symbolic and pixel input":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning curiosity algorithms")

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.CL(Computation and Language)