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: "emergent linguistic phenomena in multi agent communication games"
  Abstract: In this work, we propose a computational framework in which agents equipped with communication capabilities simultaneously play a series of referential games, where agents are trained using deep reinforcement learning. We demonstrate that the framework mirrors linguistic phenomena observed in natural language: i) the outcome of contact between communities is a function of inter- and intra-group connectivity; ii) linguistic contact either converges to the majority protocol, or in balanced cases leads to novel creole languages of lower complexity; and iii) a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed languages. We conclude that intricate properties of language evolution need not depend on complex evolved linguistic capabilities, but can emerge from simple social exchanges between perceptually-enabled agents playing communication games.

Connected through hyperedge HE-1 (induced by source paper "on the interaction between supervision and self play in emergent communication"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "capacity bandwidth and compositionality in emergent language learning"
    - "ease of teaching and language structure from emergent communication"
    - "on the pitfalls of measuring emergent communication"
    - "mastering emergent language learning to guide in simulated navigation"
    - "countering language drift via visual grounding"
    - "emergence of compositional language with deep generational transmission"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "entropy minimization in emergent languages"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "on the pitfalls of measuring emergent communication"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "compositional obverter communication learning from raw visual input"
    - "the conditional entropy bottleneck"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-3 (induced by source paper "anti efficient encoding in emergent communication"):
  with members:
    - "word order biases in deep agent emergent communication"
    - "on the pitfalls of measuring emergent communication"
    - "egg a toolkit for research on emergence of language in games"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"

Connected through hyperedge HE-4 (induced by source paper "symbol emergence as an interpersonal multimodal categorization"):
  with members:
    - "online spatial concept and lexical acquisition with simultaneous localization and mapping"
    - "serket an architecture for connecting stochastic models to realize a large scale cognitive model"
    - "emergent translation in multi agent communication"
    - "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences"
    - "sensorimotor input as a language generalisation tool a neurorobotics model for generation and generalisation of noun verb combinations with sensorimotor inputs"
    - "multi agent cooperation and the emergence of natural language"
    - "symbol emergence in robotics a survey"
    - "caffe convolutional architecture for fast feature embedding"

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)