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: "emergence of language with multi agent games learning to communicate with sequences of symbols"
  Abstract: Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI. We study a setting where two agents engage in playing a referential game and, from scratch, develop a communication protocol necessary to succeed in this game. Unlike previous work, we require that messages they exchange, both at train and test time, are in the form of a language (i.e. sequences of discrete symbols). We compare a reinforcement learning approach and one using a differentiable relaxation (straight-through Gumbel-softmax estimator) and observe that the latter is much faster to converge and it results in more effective protocols. Interestingly, we also observe that the protocol we induce by optimizing the communication success exhibits a degree of compositionality and variability (i.e. the same information can be phrased in different ways), both properties characteristic of natural languages. As the ultimate goal is to ensure that communication is accomplished in natural language, we also perform experiments where we inject prior information about natural language into our model and study properties of the resulting protocol.

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"
    - "emergent linguistic phenomena in multi agent communication games"
    - "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"

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"
    - "compositional obverter communication learning from raw visual input"
    - "scalable agent alignment via reward modeling a research direction"
    - "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"
    - "film visual reasoning with a general conditioning layer"

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"
    - "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"

Connected through hyperedge HE-4 (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"
    - "compositional obverter communication learning from raw visual input"
    - "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"
    - "asynchronous methods for deep reinforcement learning"

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"
    - "how agents see things on visual representations in an emergent language game"
    - "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 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 "xoc explainable observer classifier for explainable binary decisions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "neural network attributions a causal perspective"
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "learning attentional communication for multi agent cooperation"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tarmac targeted multi agent communication"

Connected through hyperedge HE-8 (induced by source paper "countering language drift via visual grounding"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "translating neuralese"
    - "imagination improves multimodal translation"
    - "emergent communication in a multi modal multi step referential game"
    - "learning visually grounded sentence representations"
    - "natural language does not emerge naturally in multi agent dialog"
    - "towards neural machine translation with partially aligned corpora"
    - "vse improving visual semantic embeddings with hard negatives"

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.LG(Machine Learning)