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 "event schema induction using tensor factorization with back off"):
  Abstract: The goal of Event Schema Induction(ESI) is to identify schemas of events from a corpus of documents. For example, given documents from the sports domain, we would like to infer that win(WinningPlayer, Trophy, OpponentPlayer, Location) is an important event schema for this domain. Automatic discovery of such event schemas is an important first step towards building domain-specific Knowledge Graphs (KGs). ESI has been the focus of some prior research, with generative models achieving the best performance. In this paper,we propose TFB, a tensor factorization-based method with back-off for ESI. TFB solves a novel objective to factorize Open Information Extraction (OpenIE) tuples for inducing binary schemas. Event schemas are induced out of this set of binary schemas by solving a constrained clique problem. To the best of our knowledge this is the first application of tensor factorization for the ESI problem. TFB outperforms current state-of-the-art by 52 (absolute) points gain in accuracy, while achieving 90x speedup on average. We hope to make all the code and datasets used in the paper publicly available upon publication of the paper.

Includes members:
  Member "cross sentence n ary relation extraction with graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive fractional order graph neural network")
  Member "event representations with tensor based compositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tensor decompositions in deep learning")
  Member "relation schema induction using tensor factorization with side information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "neural semantic role labeling with dependency path embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "probabilistic frame induction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "watset local global graph clustering with applications in sense and frame induction")

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.CL(Computation and Language)