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: "mutual exclusivity as a challenge for deep neural networks"
  Abstract: Strong inductive biases allow children to learn in fast and adaptable ways. Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming that if an object has one label then it does not need another. In this paper, we investigate whether or not standard neural architectures have an ME bias, demonstrating that they lack this learning assumption. Moreover, we show that their inductive biases are poorly matched to lifelong learning formulations of classification and translation. We demonstrate that there is a compelling case for designing neural networks that reason by mutual exclusivity, which remains an open challenge.

Connected through hyperedge HE-1 (induced by source paper "learning to learn words from visual scenes"):
  with members:
    - "compositional generalization in image captioning"
    - "few shot representation learning for out of vocabulary words"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "pointing novel objects in image captioning"

Connected through hyperedge HE-2 (induced by source paper "learning word referent mappings and concepts from raw inputs"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "objects that sound"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "building machines that learn and think like people"

Connected through hyperedge HE-3 (induced by source paper "learning to learn words from narrated video"):
  with members:
    - "compositional generalization in image captioning"
    - "few shot representation learning for out of vocabulary words"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "compositional generalization through meta sequence to sequence learning"

Connected through hyperedge HE-4 (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"
    - "human few shot learning of compositional instructions"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "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-5 (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"
    - "human few shot learning of compositional instructions"
    - "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"

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)