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: "bootstrapping distantly supervised ie using joint learning and small well structured corpora"
  Abstract: We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction. We combine this with a novel use of document structure: in some small, well-structured corpora, sections can be identified that correspond to relation arguments, and distantly-labeled examples from such sections tend to have good precision. Using these as seeds we extract additional relation examples by applying label propagation on a graph composed of noisy examples extracted from a large unstructured testing corpus. Combined with the soft constraint that concept examples should have the same type as the second argument of the relation, we get significant improvements over several state-of-the-art approaches to distantly-supervised relation extraction.

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"
    - "distant ie by bootstrapping using lists and document structure"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "using graphs of classifiers to impose declarative constraints on semi supervised learning"):
  with members:
    - "distant ie by bootstrapping using lists and document structure"

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)