human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "information minimization in emergent languages"
  Abstract: There is a growing interest in studying the languages emerging when neural agents are jointly trained to solve tasks that require communication through discrete messages. We investigate here the information-theoretic complexity of such languages, focusing on the most basic two-agent, one-symbol, one-exchange setup. We find that, under common training procedures, the emergent languages are subject to an information minimization pressure: The mutual information between the communicating agent's inputs and the messages is close to the minimum that still allows the task to be solved. After verifying this information minimization property, we perform experiments showing that a stronger discrete-channel-driven information minimization pressure leads to increased robustness to overfitting and to adversarial attacks. We conclude by discussing the implications of our findings for the studies of artificial and natural language emergence, and for representation learning.

Connected through hyperedge HE-1 (induced by source paper "egg a toolkit for research on emergence of language in games"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "on the pitfalls of measuring emergent communication"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"

Connected through hyperedge HE-2 (induced by source paper "focus on what s informative and ignore what s not communication strategies in a referential game"):
  with members:
    - "word order biases in deep agent emergent communication"
    - "meaning to form measuring systematicity as information"
    - "egg a toolkit for research on emergence of language in games"
    - "compositional obverter communication learning from raw visual input"
    - "how agents see things on visual representations in an emergent language game"
    - "emergent communication through negotiation"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "emergence of grounded compositional language in multi agent populations"

Question: Please tell me which class the center node 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)