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 "cesi canonicalizing open knowledge bases using embeddings and side information"):
  Abstract: Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large open Knowledge Bases (KBs). The noun phrases (NPs) and relation phrases in such open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of open KBs as clustering over manually-defined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose CESI — a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI’s effectiveness. We plan to make publicly available all the data and code used in the paper.

Includes members:
  Member "relation schema induction using tensor factorization with side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "holographic embeddings of knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graphs")
  Member "probabilistic similarity logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non parametric learning of gaifman models")
  Member "unsupervised methods for determining object and relation synonyms on the web":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural graph embedding methods for natural language processing")

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.IR(Information Retrieval)