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: "shapeworld a new test methodology for multimodal language understanding"
  Abstract: We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities. In this approach, artificial data is automatically generated according to the experimenter's specifications. The content of the data, both during training and evaluation, can be controlled in detail, which enables tasks to be created that require true generalization abilities, in particular the combination of previously introduced concepts in novel ways. We demonstrate the potential of our methodology by evaluating various visual question answering models on four different tasks, and show how our framework gives us detailed insights into their capabilities and limitations. By open-sourcing our framework, we hope to stimulate progress in the field of multimodal language understanding.

Connected through hyperedge HE-1 (induced by source paper "a benchmark for systematic generalization in grounded language understanding"):
  with members:
    - "learning compositional rules via neural program synthesis"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "emergent systematic generalization in a situated agent"
    - "compositional generalization through meta sequence to sequence learning"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "modeling question asking using neural program generation"
    - "babyai a platform to study the sample efficiency of grounded language learning"

Connected through hyperedge HE-2 (induced by source paper "multimodal generative models for compositional representation learning"):
  with members:
    - "factorized inference in deep markov models for incomplete multimodal time series"
    - "measuring compositionality in representation learning"
    - "vae gans for probabilistic compressive image recovery uncertainty analysis"
    - "shapeglot learning language for shape differentiation"
    - "neural autoregressive flows"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "multimodal matching using a hybrid convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "closure assessing systematic generalization of clevr models"):
  with members:
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "systematic generalization what is required and can it be learned"
    - "jump to better conclusions scan both left and right"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "rearranging the familiar testing compositional generalization in recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "going beneath the surface evaluating image captioning for grammaticality truthfulness and diversity"):
  with members:
    - "object counts bringing explicit detections back into image captioning"
    - "a call for clarity in reporting bleu scores"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "convolutional image captioning"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-5 (induced by source paper "shaping visual representations with language for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "using natural language for reward shaping in reinforcement learning"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "grounding visual explanations"
    - "e snli natural language inference with natural language explanations"
    - "learning to understand goal specifications by modelling reward"

Connected through hyperedge HE-6 (induced by source paper "learning from implicit information in natural language instructions for robotic manipulations"):
  with members:
    - "semantic relational object tracking"
    - "improving grounded natural language understanding through human robot dialog"
    - "using syntax to ground referring expressions in natural images"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "neural modular control for embodied question answering"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-7 (induced by source paper "emergent linguistic phenomena in multi agent communication games"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "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"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "emergent translation in multi agent communication"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-8 (induced by source paper "what is needed for simple spatial language capabilities in vqa"):
  with members:
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "a dataset and architecture for visual reasoning with a working memory"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

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)