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: "compositional obverter communication learning from raw visual input"
  Abstract: One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. hand- engineered features). Humans, however, do not learn to communicate based on well-summarized features. In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols. The agents play an image description game where the image contains factors such as colors and shapes. We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding. Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.

Connected through hyperedge HE-1 (induced by source paper "emergent communication with world models"):
  with members:
    - "biases for emergent communication in multi agent reinforcement learning"
    - "on the pitfalls of measuring emergent communication"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"

Connected through hyperedge HE-2 (induced by source paper "incentivizing the emergence of grounded discrete communication between general agents"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "scalable agent alignment via reward modeling a research direction"
    - "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"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-3 (induced by source paper "emergence of pragmatics from referential game between theory of mind agents"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "planning inference and pragmatics in sequential language games"
    - "emergence of communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication through negotiation"
    - "emergent communication in a multi modal multi step referential game"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-4 (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"
    - "how agents see things on visual representations in an emergent language game"
    - "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-5 (induced by source paper "compositional languages emerge in a neural iterated learning model"):
  with members:
    - "measuring compositionality in representation learning"
    - "ease of teaching and language structure from emergent communication"
    - "emergence of compositional language with deep generational transmission"
    - "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"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning by abstraction the neural state machine"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "measuring compositionality in representation learning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "answer them all toward universal visual question answering models"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "modeling conceptual understanding in image reference games"):
  with members:
    - "emergence of compositional language with deep generational transmission"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "faithful multimodal explanation for visual question answering"
    - "interactive agent modeling by learning to probe"
    - "machine theory of mind"

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.AI(Artificial Intelligence)