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: "neural transformation machine a new architecture for sequence to sequence learning"
  Abstract: We propose Neural Transformation Machine (NTram), a novel 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 recent Neural Turing Machines [8], we store the intermediate representations in stacked layers of memories, and use read-write operations on the memories to realize the nonlinear transformations of those representations. Those transformations are designed in advance but the parameters are learned from data. Through layer-by-layer transformations, NTram can model complicated relations necessary for applications such as machine translation between distant languages. The architecture can be trained with normal back-propagation on parallel texts, and the learning can be easily scaled up to a large corpus. NTram is broad enough to subsume the state-of-the-art neural translation model in [2] as its special case, while significantly improves upon the model with its deeper architecture. Remarkably, NTram, 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 "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"
    - "a deep memory based 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-2 (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"

Connected through hyperedge HE-3 (induced by source paper "interactive attention for neural machine translation"):
  with members:
    - "memory enhanced decoder for neural machine translation"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with external phrase memory"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "effective approaches to attention based neural machine translation"
    - "minimum risk training for neural machine translation"
    - "neural turing machines"

Connected through hyperedge HE-4 (induced by source paper "memory enhanced decoder for neural machine translation"):
  with members:
    - "long short term memory networks for machine reading"
    - "modeling coverage for neural machine translation"
    - "context dependent translation selection using convolutional neural network"
    - "effective approaches to attention based neural machine translation"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "how to construct deep recurrent neural 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.CL(Computation and Language)