human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "robust and scalable differentiable neural computer for question answering"
  Abstract: Deep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem solver which can be used in a wide range of tasks. But in reality, it is hard to apply this model to new tasks. We analyze the DNC and identify possible improvements within the application of question answering. This motivates a more robust and scalable DNC (rsDNC). The objective precondition is to keep the general character of this model intact while making its application more reliable and speeding up its required training time. The rsDNC is distinguished by a more robust training, a slim memory unit and a bidirectional architecture. We not only achieve new state-of-the-art performance on the bAbI task, but also minimize the performance variance between different initializations. Furthermore, we demonstrate the simplified applicability of the rsDNC to new tasks with passable results on the CNN RC task without adaptions.

Connected through hyperedge HE-1 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory"):
  with members:
    - "learning to remember more with less memorization"
    - "dual control memory augmented neural networks for treatment recommendations"
    - "memory augmented neural networks for predictive process analytics"
    - "rosetta large scale system for text detection and recognition in images"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "handwritten digit string recognition by combination of residual network and rnn ctc"
    - "implicit language model in lstm for ocr"
    - "reading scene text with attention convolutional sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "learning to remember more with less memorization"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "neural speed reading via skim rnn"
    - "attention is all you need"
    - "dilated recurrent neural networks"
    - "learning to skim text"
    - "unbounded cache model for online language modeling with open vocabulary"
    - "adaptive computation time for recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "neural stored program memory"):
  with members:
    - "learning to remember more with less memorization"
    - "improving differentiable neural computers through memory masking de allocation and link distribution sharpness control"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "continual learning through synaptic intelligence"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "learning to compose neural networks for question answering"
    - "end to end memory networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-4 (induced by source paper "improving differentiable neural computers through memory masking de allocation and link distribution sharpness control"):
  with members:
    - "adaptive computation time for recurrent neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "tracking the world state with recurrent entity networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "self delimiting neural networks"

Connected through hyperedge HE-5 (induced by source paper "partially non recurrent controllers for memory augmented neural networks"):
  with members:
    - "memory augmented neural networks with wormhole connections"
    - "lie access neural turing machine"
    - "tracking the world state with recurrent entity networks"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "wavenet a generative model for raw audio"
    - "learning to transduce with unbounded memory"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "reinforcement learning neural turing machines revised"

Question: Please tell me which class the center node 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)