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 "learning to remember more with less memorization"):
  Abstract: Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize that this scheme of writing is suboptimal in memory utilization and introduces redundant computation. To validate our hypothesis, we derive a theoretical bound on the amount of information stored in a RAM-like system and formulate an optimization problem that maximizes the bound. The proposed solution dubbed Uniform Writing is proved to be optimal under the assumption of equal timestep contributions. To relax this assumption, we introduce modifications to the original solution, resulting in a solution termed Cached Uniform Writing. This method aims to balance between maximizing memorization and forgetting via overwriting mechanisms. Through an extensive set of experiments, we empirically demonstrate the advantages of our solutions over other recurrent architectures, claiming the state-of-the-arts in various sequential modeling tasks.

Includes members:
  Member "learning longer term dependencies in rnns with auxiliary losses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformer xl attentive language models beyond a fixed length context")
  Member "robust and scalable differentiable neural computer for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory")
  Member "dual memory neural computer for asynchronous two view sequential learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self attentive associative memory")
  Member "neural speed reading via skim rnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "dilated recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving the gating mechanism of recurrent neural networks")
  Member "learning to skim text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memo a deep network for flexible combination of episodic memories")
  Member "unbounded cache model for online language modeling with open vocabulary":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "large memory layers with product keys")

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.LG(Machine Learning)