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: "systematic generalization what is required and can it be learned"
  Abstract: Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We find that end-to-end methods from prior work often learn inappropriate layouts or parametrizations that do not facilitate systematic generalization. Our results suggest that, in addition to modularity, systematic generalization in language understanding may require explicit regularizers or priors.

Connected through hyperedge HE-1 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration"):
  with members:
    - "emergent systematic generalization in a situated agent"
    - "skew fit state covering self supervised reinforcement learning"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "monet unsupervised scene decomposition and representation"
    - "extending machine language models toward human level language understanding"
    - "using natural language for reward shaping in reinforcement learning"
    - "on the limitations of representing functions on sets"
    - "from language to goals inverse reinforcement learning for vision based instruction following"

Connected through hyperedge HE-2 (induced by source paper "evaluating logical generalization in graph neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "pytorch an imperative style high performance deep learning library"
    - "on training recurrent neural networks for lifelong learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "environmental drivers of systematicity and generalization in a situated agent"):
  with members:
    - "human few shot learning of compositional instructions"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "compositional generalization through meta sequence to sequence learning"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "understanding grounded language learning agents"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-5 (induced by source paper "compositional properties of emergent languages in deep learning"):
  with members:
    - "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 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 "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"

Connected through hyperedge HE-7 (induced by source paper "videonavqa bridging the gap between visual and embodied question answering"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "multi target embodied question answering"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "habitat a platform for embodied ai research"
    - "murel multimodal relational reasoning for visual question answering"
    - "learning visual question answering by bootstrapping hard attention"
    - "blindfold baselines for embodied qa"

Connected through hyperedge HE-8 (induced by source paper "ordered memory"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "cooperative learning of disjoint syntax and semantics"
    - "listops a diagnostic dataset for latent tree learning"
    - "improved sentence modeling using suffix bidirectional lstm"
    - "deep contextualized word representations"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "rearranging the familiar testing compositional generalization in recurrent networks"

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)