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: "dual memory neural computer for asynchronous two view sequential learning"
  Abstract: One of the core task in multi-view learning is to capture all relations among views. For sequential data, the relations not only span across views, but also extend throughout the view length to form long-term intra-view and cross-view interactions. In this paper, we present a new memory augmented neural network model that aims to model these complex interactions between two sequential views that are asynchronous. Our model uses two neural encoders for reading from and writing to two external memories for encoding input views. The intra-view interactions and the long-term dependencies are captured by the use of memories during this encoding process. There are two modes of memory accessing in our system: late-fusion and early-fusion, corresponding to late and early cross-view interactions. In late-fusion mode, the two memories are separated, containing only view-specific contents. In the early-fusion mode, the two memories share the same addressing space, allowing cross-memory accessing. In both cases, the knowledge from the memories finally will be synthesized by a decoder to make predictions over the output space. The resulting dual memory neural computer is demonstrated on various of experiments, from the synthetic sum of two sequences task to the tasks of drug prescription and disease progressions in healthcare. The results show improved performance over both traditional algorithms and deep learning methods designed for multi-view problems.

Connected through hyperedge HE-1 (induced by source paper "self attentive associative memory"):
  with members:
    - "neural stored program memory"
    - "learning to remember more with less memorization"
    - "metalearned neural memory"
    - "bilinear attention networks"
    - "relational recurrent neural networks"
    - "learning to reason with third order tensor products"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-2 (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"
    - "robust and scalable differentiable neural computer for question answering"
    - "rosetta large scale system for text detection and recognition in images"
    - "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-3 (induced by source paper "learning to remember more with less memorization"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "robust and scalable differentiable neural computer for question answering"
    - "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-4 (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"
    - "robust and scalable differentiable neural computer for question answering"
    - "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-5 (induced by source paper "doctor2vec dynamic doctor representation learning for clinical trial recruitment"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gamenet graph augmented memory networks for recommending medication combination"
    - "attention is all you need"
    - "key value memory networks for directly reading documents"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "end to end memory networks"

Connected through hyperedge HE-6 (induced by source paper "explicit blurred memory network for analyzing patient electronic health records"):
  with members:
    - "attention is not explanation"
    - "memory augmented neural networks with wormhole connections"
    - "condensed memory networks for clinical diagnostic inferencing"
    - "end to end memory networks"
    - "neural turing machines"

Connected through hyperedge HE-7 (induced by source paper "gamenet graph augmented memory networks for recommending medication combination"):
  with members:
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "safe medicine recommendation via medical knowledge graph embedding"
    - "key value memory networks for directly reading documents"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "end to end memory networks"

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.LG(Machine Learning)