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: "human few shot learning of compositional instructions"
  Abstract: People learn in fast and flexible ways that have not been emulated by machines. Once a person learns a new verb "dax," he or she can effortlessly understand how to "dax twice," "walk and dax," or "dax vigorously." There have been striking recent improvements in machine learning for natural language processing, yet the best algorithms require vast amounts of experience and struggle to generalize new concepts in compositional ways. To better understand these distinctively human abilities, we study the compositional skills of people through language-like instruction learning tasks. Our results show that people can learn and use novel functional concepts from very few examples (few-shot learning), successfully applying familiar functions to novel inputs. People can also compose concepts in complex ways that go beyond the provided demonstrations. Two additional experiments examined the assumptions and inductive biases that people make when solving these tasks, revealing three biases: mutual exclusivity, one-to-one mappings, and iconic concatenation. We discuss the implications for cognitive modeling and the potential for building machines with more human-like language learning capabilities.

Connected through hyperedge HE-1 (induced by source paper "learning compositional rules via neural program synthesis"):
  with members:
    - "learning to infer program sketches"
    - "learning to prove theorems via interacting with proof assistants"
    - "compositional generalization through meta sequence to sequence learning"
    - "write execute assess program synthesis with a repl"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "environmental drivers of systematicity and generalization in a situated agent"):
  with members:
    - "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"
    - "systematic generalization what is required and can it be learned"
    - "understanding grounded language learning agents"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "ecological semantics programming environments for situated language understanding"):
  with members:
    - "bringing stories alive generating interactive fiction worlds"
    - "environmental drivers of systematicity and generalization in a situated agent"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "extending machine language models toward human level language understanding"
    - "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"
    - "do neural language representations learn physical commonsense"
    - "a survey of reinforcement learning informed by natural language"
    - "how large are lions inducing distributions over quantitative attributes"

Connected through hyperedge HE-4 (induced by source paper "compositional neural machine translation by removing the lexicon from syntax"):
  with members:
    - "compound probabilistic context free grammars for grammar induction"
    - "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"
    - "attention is all you need"
    - "data recombination for neural semantic parsing"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "compositional generalization through meta sequence to sequence learning"):
  with members:
    - "learning to infer program sketches"
    - "the omniglot challenge a 3 year progress report"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "systematic generalization what is required and can it be learned"
    - "jump to better conclusions scan both left and right"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-6 (induced by source paper "good enough compositional data augmentation"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "learning data manipulation for augmentation and weighting"
    - "improving text to sql evaluation methodology"
    - "deep contextualized word representations"
    - "jump to better conclusions scan both left and right"
    - "deep learning a critical appraisal"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "analyzing machine learned representations a natural language case study"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "analyzing compositionality sensitivity of nli models"
    - "analysis methods in neural language processing a survey"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "adventure adversarial training for textual entailment with knowledge guided examples"

Connected through hyperedge HE-8 (induced by source paper "compositional generalization for primitive substitutions"):
  with members:
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "efficient lifelong learning with a gem"
    - "jump to better conclusions scan both left and right"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "automatically composing representation transformations as a means for generalization"
    - "memory aware synapses learning what not to forget"
    - "attention is all you need"

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)