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 "capacity bandwidth and compositionality in emergent language learning"):
  Abstract: Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compositionality. We note that most of those works have focused on communicative bandwidth as being of primary importance. While important, it is not the only contributing factor. In this paper, we investigate the learning biases that affect the efficacy and compositionality of emergent languages. Our foremost contribution is to explore how capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.

Includes members:
  Member "linguistic generalization and compositionality in modern artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "is the red square big malevic modeling adjectives leveraging visual contexts")
  Member "on the interaction between supervision and self play in emergent communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploring structural inductive biases in emergent communication")
  Member "anti efficient encoding in emergent communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "the compositionality of neural networks integrating symbolism and connectionism":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discovering the compositional structure of vector representations with role learning networks")
  Member "word order biases in deep agent emergent communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "egg a toolkit for research on emergence of language in games")
  Member "measuring compositionality in representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "countering language drift via visual grounding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "compositional generalization through meta sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to learn words from visual scenes")

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)