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: "how agents see things on visual representations in an emergent language game"
  Abstract: There is growing interest in the language developed by agents interacting in emergent-communication settings. Earlier studies have focused on the agents' symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017) and investigate the representations the agents develop during their evolving interaction. We find that the agents establish successful communication by inducing visual representations that almost perfectly align with each other, but, surprisingly, do not capture the conceptual properties of the objects depicted in the input images. We conclude that, if we are interested in developing language-like communication systems, we must pay more attention to the visual semantics agents associate to the symbols they use.

Connected through hyperedge HE-1 (induced by source paper "towards graph representation learning in emergent communication"):
  with members:
    - "capacity bandwidth and compositionality in emergent language learning"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "egg a toolkit for research on emergence of language in games"
    - "pytorch an imperative style high performance deep learning library"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-2 (induced by source paper "on emergent communication in competitive multi agent teams"):
  with members:
    - "measuring compositionality in representation learning"
    - "on the pitfalls of measuring emergent communication"
    - "strong and simple baselines for multimodal utterance embeddings"
    - "emergence of compositional language with deep generational transmission"
    - "multimodal language analysis with recurrent multistage fusion"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "emergence of communication in an interactive world with consistent speakers"

Connected through hyperedge HE-3 (induced by source paper "co evolution of language and agents in referential games"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "miss tools and mr fruit emergent communication in agents learning about object affordances"
    - "emergence of compositional language with deep generational transmission"
    - "compositional obverter communication learning from raw visual input"
    - "darts differentiable architecture search"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "efficient neural architecture search via parameter sharing"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"

Connected through hyperedge HE-4 (induced by source paper "exploring structural inductive biases in emergent communication"):
  with members:
    - "on the interaction between supervision and self play in emergent communication"
    - "measuring compositionality in representation learning"
    - "ease of teaching and language structure from emergent communication"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "egg a toolkit for research on emergence of language in games"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-5 (induced by source paper "compositional properties of emergent languages in deep learning"):
  with members:
    - "systematic generalization what is required and can it be learned"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "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"
    - "emergence of grounded compositional language in multi agent populations"
    - "film visual reasoning with a general conditioning layer"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "adversarial language games for advanced natural language intelligence"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "egg a toolkit for research on emergence of language in games"
    - "robust neural machine translation with doubly adversarial inputs"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "robust machine comprehension models via adversarial training"

Connected through hyperedge HE-7 (induced by source paper "cooperative image captioning"):
  with members:
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "evaluating text to image matching using binary image selection bison"
    - "a neural compositional paradigm for image captioning"
    - "learning to describe differences between pairs of similar images"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "natural language does not emerge naturally in multi agent dialog"
    - "speaking the same language matching machine to human captions by adversarial training"

Connected through hyperedge HE-8 (induced by source paper "entropy minimization in emergent languages"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "emergent linguistic phenomena in multi agent communication games"
    - "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"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"

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)