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: "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
  Abstract: We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on this http URL

Connected through hyperedge HE-1 (induced by source paper "zero shot cross lingual transfer with meta learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "x wikire a large multilingual resource for relation extraction as machine comprehension"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "on first order meta learning algorithms"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "learning to few shot learn across diverse natural language classification tasks"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert rediscovers the classical nlp pipeline"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-3 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "language models as knowledge bases"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "multi task deep neural networks for natural language understanding"
    - "matching the blanks distributional similarity for relation learning"

Connected through hyperedge HE-4 (induced by source paper "ernie enhanced language representation with informative entities"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "improving question answering by commonsense based pre training"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ultra fine entity typing"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

Connected through hyperedge HE-5 (induced by source paper "few shot text classification with distributional signatures"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "huggingface s transformers state of the art natural language processing"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "multi source domain adaptation with mixture of experts"
    - "how to train your maml"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "metric learning for dynamic text classification"):
  with members:
    - "hyperbolic interaction model for hierarchical multi label classification"
    - "hyperbolic image embeddings"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "multi source domain adaptation with mixture of experts"
    - "deep contextualized word representations"
    - "hyperbolic neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-7 (induced by source paper "docred a large scale document level relation extraction dataset"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "improving the transformer translation model with document level context"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross sentence n ary relation extraction with graph lstms"

Connected through hyperedge HE-8 (induced by source paper "neural snowball for few shot relation learning"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "deep comparison relation columns for few shot learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "self ensembling for visual domain adaptation"
    - "unsupervised open relation extraction"
    - "learning to compare relation network for few shot learning"

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.LG(Machine Learning)