human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "few shot text classification with distributional signatures"):
  Abstract: In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging-lexical features highly informative for one task maybe insignificant for another. Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns. Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words. We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification). Our code is available at this https URL.

Includes members:
  Member "meta learning with differentiable convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "huggingface s transformers state of the art natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised question decomposition for question answering")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting meta learning as supervised learning")
  Member "on first order meta learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "reptile a scalable metalearning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "multi source domain adaptation with mixture of experts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "how to train your maml":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")

Question: Please tell me which class the source paper 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)