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: "extrapolation in nlp"
  Abstract: We argue that extrapolation to examples outside the training space will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We show that this is true for two popular models: the Decomposable Attention Model and word2vec.

Connected through hyperedge HE-1 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "deep contextualized word representations"
    - "unsupervised word discovery with segmental neural language models"

Connected through hyperedge HE-2 (induced by source paper "location attention for extrapolation to longer sequences"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "learning compositionally through attentive guidance"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "generalisation in humans and deep neural networks"
    - "deep learning a critical appraisal"
    - "the fine line between linguistic generalization and failure in seq2seq attention models"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "factors for the generalisation of identity relations by neural networks"):
  with members:
    - "feed forward neural networks need inductive bias to learn equality relations"
    - "neural arithmetic logic units"
    - "relational inductive bias for physical construction in humans and machines"
    - "prototypical networks for few shot learning"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-4 (induced by source paper "modelling identity rules with neural networks"):
  with members:
    - "learning inductive biases with simple neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "measuring abstract reasoning in neural networks"
    - "dynamic routing between capsules"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "feed forward neural networks need inductive bias to learn equality relations"):
  with members:
    - "logical rule induction and theory learning using neural theorem proving"
    - "relational inductive bias for physical construction in humans and machines"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "adam a method for stochastic optimization"

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)