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: "one shot and few shot learning of word embeddings"
  Abstract: Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by leveraging what the syntax and semantics of the surrounding words tells us. Here, we draw inspiration from this to highlight a simple technique by which deep recurrent networks can similarly exploit their prior knowledge to learn a useful representation for a new word from little data. This could make natural language processing systems much more flexible, by allowing them to learn continually from the new words they encounter.

Connected through hyperedge HE-1 (induced by source paper "zero shot task adaptation by homoiconic meta mapping"):
  with members:
    - "lgm net learning to generate matching networks for few shot learning"
    - "online meta learning"
    - "learnable embedding space for efficient neural architecture compression"
    - "zero shot task transfer"
    - "task2vec task embedding for meta learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "meta learning with latent embedding optimization"
    - "universal successor features approximators"

Connected through hyperedge HE-2 (induced by source paper "text classification algorithms a survey"):
  with members:
    - "rmdl random multimodel deep learning for classification"
    - "analysis of railway accidents narratives using deep learning"
    - "deep contextualized word representations"
    - "an improvement of data classification using random multimodel deep learning rmdl"
    - "gakco a fast gapped k mer string kernel using counting"
    - "learning important features through propagating activation differences"
    - "opening the black box of deep neural networks via information"
    - "improving the accuracy of pre trained word embeddings for sentiment analysis"

Connected through hyperedge HE-3 (induced by source paper "antonym synonym classification based on new sub space embeddings"):
  with members:
    - "deep contextualized word representations"
    - "distinguishing antonyms and synonyms in a pattern based neural network"
    - "fasttext zip compressing text classification models"
    - "specialising word vectors for lexical entailment"
    - "poincar e embeddings for learning hierarchical representations"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "rearranging the familiar testing compositional generalization in recurrent networks"):
  with members:
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "building machines that learn and think like people"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "sequence to sequence learning with neural 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)