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 graph representation learning in emergent communication"):
  Abstract: Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we flatten the complex representation of entities and their attributes into a single word or a sentence. In this paper we use graph convolutional networks to support the evolution of language and cooperation in multi-agent systems. Motivated by an image-based referential game, we propose a graph referential game with varying degrees of complexity, and we provide strong baseline models that exhibit desirable properties in terms of language emergence and cooperation. We show that the emerged communication protocol is robust, that the agents uncover the true factors of variation in the game, and that they learn to generalize beyond the samples encountered during training.

Includes members:
  Member "capacity bandwidth and compositionality in emergent language learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "deep graph library towards efficient and scalable deep learning on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visual semantic graph attention networks for human object interaction detection")
  Member "egg a toolkit for research on emergence of language in games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploring structural inductive biases in emergent communication")
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  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 "on emergent communication in competitive multi agent teams")
  Member "graph2seq graph to sequence learning with attention based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  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 "emergent communication in a multi modal multi step referential game":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the interaction between supervision and self play in emergent communication")

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.LG(Machine Learning)