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: "does william shakespeare really write hamlet knowledge representation learning with confidence"
  Abstract: Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall human knowledge is innumerable that still grows explosively and changes frequently, knowledge construction and update inevitably involve automatic mechanisms with less human supervision, which usually bring in plenty of noises and conflicts to KGs. However, most conventional knowledge representation learning methods assume that all triple facts in existing KGs share the same significance without any noises. To address this problem, we propose a novel confidence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with confidence simultaneously. Specifically, we introduce the triple confidence to conventional translation-based methods for knowledge representation learning. To make triple confidence more flexible and universal, we only utilize the internal structural information in KGs, and propose three kinds of triple confidences considering both local and global structural information. In experiments, We evaluate our models on knowledge graph noise detection, knowledge graph completion and triple classification. Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.

Connected through hyperedge HE-1 (induced by source paper "error detection in knowledge graphs path ranking embeddings or both"):
  with members:
    - "logic rules powered knowledge graph embedding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"
    - "learning multi relational semantics using neural embedding models"

Connected through hyperedge HE-2 (induced by source paper "scef a support confidence aware embedding framework for knowledge graph refinement"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "knowledge graph error detection and completion"):
  with members:
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "compositional learning of relation paths embedding for knowledge base completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "medtruth a semi supervised approach to discovering knowledge condition information from multi source medical data"):
  with members:
    - "crowdsourcing ground truth for medical relation extraction"
    - "truth finding on the deep web is the problem solved"
    - "a survey on truth discovery"
    - "efficient estimation of word representations in vector space"

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)