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 "learning to few shot learn across diverse natural language classification tasks"):
  Abstract: Self-supervised pre-training of transformer models has shown enormous success in improving performance on a number of downstream tasks. However, fine-tuning on a new task still requires large amounts of task-specific labelled data to achieve good performance. We consider this problem of learning to generalize to new tasks with few examples as a meta-learning problem. While meta-learning has shown tremendous progress in recent years, its application is still limited to simulated problems or problems with limited diversity across tasks. We develop a novel method, LEOPARD, which enables optimization-based meta-learning across tasks with different number of classes, and evaluate existing methods on generalization to diverse NLP classification tasks. LEOPARD is trained with the state-of-the-art transformer architecture and shows strong generalization to tasks not seen at all during training, with as few as 8 examples per label. On 16 NLP datasets, across a diverse task-set such as entity typing, relation extraction, natural language inference, sentiment analysis, and several other text categorization tasks, we show that LEOPARD learns better initial parameters for few-shot learning than self-supervised pre-training or multi-task training, outperforming many strong baselines, for example, increasing F1 from 49% to 72%.

Includes members:
  Member "parameter efficient transfer learning for nlp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "contextual lensing of universal sentence representations")
  Member "bert rediscovers the classical nlp pipeline":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "meta learning for low resource natural language generation in task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "learning and evaluating general linguistic intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "to tune or not to tune adapting pretrained representations to diverse tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual lensing of universal sentence representations")
  Member "multi task deep neural networks for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning from explanations with neural execution tree")
  Member "meta dataset a dataset of datasets for learning to learn from few examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)