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: "a deep memory based architecture for sequence to sequence learning"
  Abstract: We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence learning, which performs the task through a series of nonlinear transformations from the representation of the input sequence (e.g., a Chinese sentence) to the final output sequence (e.g., translation to English). Inspired by the recently proposed Neural Turing Machine (Graves et al., 2014), we store the intermediate representations in stacked layers of memories, and use read-write operations on the memories to realize the nonlinear transformations between the representations. The types of transformations are designed in advance but the parameters are learned from data. Through layer-by-layer transformations, DEEPMEMORY can model complicated relations between sequences necessary for applications such as machine translation between distant languages. The architecture can be trained with normal back-propagation on sequenceto-sequence data, and the learning can be easily scaled up to a large corpus. DEEPMEMORY is broad enough to subsume the state-of-the-art neural translation model in (Bahdanau et al., 2015) as its special case, while significantly improving upon the model with its deeper architecture. Remarkably, DEEPMEMORY, being purely neural network-based, can achieve performance comparable to the traditional phrase-based machine translation system Moses with a small vocabulary and a modest parameter size.

Connected through hyperedge HE-1 (induced by source paper "dynamic layer aggregation for neural machine translation with routing by agreement"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "exploiting deep representations for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "multi head attention with disagreement regularization"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "thumt an open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "long short term memory networks for machine reading"):
  with members:
    - "learning to compose neural networks for question answering"
    - "recurrent memory network for language modeling"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"
    - "a neural attention model for abstractive sentence summarization"
    - "neural transformation machine a new architecture for sequence to sequence learning"
    - "transition based dependency parsing with stack long short term memory"
    - "learning to transduce with unbounded memory"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation advised by statistical machine translation"):
  with members:
    - "character based neural machine translation"
    - "context gates for neural machine translation"
    - "neural machine translation with reconstruction"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with external phrase memory"

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)