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 "understanding memory modules on learning simple algorithms"):
  Abstract: Recent work has shown that memory modules are crucial for the generalization ability of neural networks on learning simple algorithms. However, we still have little understanding of the working mechanism of memory modules. To alleviate this problem, we apply a two-step analysis pipeline consisting of first inferring hypothesis about what strategy the model has learned according to visualization and then verify it by a novel proposed qualitative analysis method based on dimension reduction. Using this method, we have analyzed two popular memory-augmented neural networks, neural Turing machine and stack-augmented neural network on two simple algorithm tasks including reversing a random sequence and evaluation of arithmetic expressions. Results have shown that on the former task both models can learn to generalize and on the latter task only the stack-augmented model can do so. We show that different strategies are learned by the models, in which specific categories of input are monitored and different policies are made based on that to change the memory.

Includes members:
  Member "understanding recurrent neural state using memory signatures":
    also connected through other hyperedges:
      (none)
  Member "interpreting recurrent and attention based neural models a case study on natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")
  Member "state gradients for rnn memory analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "explaining recurrent neural network predictions in sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning")
  Member "memory augmented neural networks with wormhole connections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving the gating mechanism of recurrent neural networks")
  Member "scaling memory augmented neural networks with sparse reads and writes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory based graph networks")
  Member "dynamic neural turing machine with soft and hard addressing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to follow directions in street view")

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)