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: "distant ie by bootstrapping using lists and document structure"
  Abstract: Distant labeling for information extraction (IE) suffers from noisy training data. We describe a way of reducing the noise associated with distant IE by identifying coupling constraints between potential instance labels. As one example of coupling, items in a list are likely to have the same label. A second example of coupling comes from analysis of document structure: in some corpora, sections can be identified such that items in the same section are likely to have the same label. Such sections do not exist in all corpora, but we show that augmenting a large corpus with coupling constraints from even a small, well-structured corpus can improve performance substantially, doubling F1 on one task.

Connected through hyperedge HE-1 (induced by source paper "tackling long tailed relations and uncommon entities in knowledge graph completion"):
  with members:
    - "on first order meta learning algorithms"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "low shot learning from imaginary data"
    - "one shot relational learning for knowledge graphs"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "open world knowledge graph completion"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning with declaratively specified entropy constraints"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "semi supervised classification with graph convolutional networks"
    - "bootstrapping distantly supervised ie using joint learning and small well structured corpora"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "an enhanced computational feature selection method for medical synonym identification via bilingualism and multi corpus training"):
  with members:
    - "component enhanced chinese character embeddings"
    - "medical synonym extraction with concept space models"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "the google similarity distance"

Connected through hyperedge HE-4 (induced by source paper "using graphs of classifiers to impose declarative constraints on semi supervised learning"):
  with members:
    - "bootstrapping distantly supervised ie using joint learning and small well structured corpora"

Connected through hyperedge HE-5 (induced by source paper "bootstrapping distantly supervised ie using joint learning and small well structured corpora"):
  with members:
    - "scaling graph based semi supervised learning to large number of labels using count min sketch"

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)