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 "completion reasoning emulation for the description logic el"):
  Abstract: We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner's and LSTM's predictions on corrupt data with correct answers.

Includes members:
  Member "el embeddings geometric construction of models for the description logic el":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the keen universe an ecosystem for knowledge graph embeddings with a focus on reproducibility and transferability")
  Member "reasoning over rdf knowledge bases using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explainable deep rdfs reasoner")
  Member "deeppath a reinforcement learning method for knowledge graph reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "neural symbolic learning and reasoning a survey and interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "chains of reasoning over entities relations and text using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings")

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.AI(Artificial Intelligence)