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: "twin networks matching the future for sequence generation"
  Abstract: We propose a simple technique for encouraging generative RNNs to plan ahead. We train a "backward" recurrent network to generate a given sequence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model. The backward network is used only during training, and plays no role during sampling or inference. We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states). We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.

Connected through hyperedge HE-1 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pre trained language model representations for language generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "defending against neural fake news"
    - "albert a lite bert for self supervised learning of language representations"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "icap interative image captioning with predictive text"):
  with members:
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "annotating object instances with a polygon rnn"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "self critical sequence training for image captioning"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-3 (induced by source paper "distilling the knowledge of bert for text generation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-4 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "deconvolution based global decoding for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-5 (induced by source paper "synchronous bidirectional inference for neural sequence generation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bridging the gap between training and inference for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "self attention with relative position representations"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "synchronous bidirectional neural machine translation"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "modeling past and future for neural machine translation"
    - "learning to decode for future success"

Connected through hyperedge HE-7 (induced by source paper "sequence generation from both sides to the middle"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "accelerating neural transformer via an average attention network"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-8 (induced by source paper "assessing incrementality in sequence to sequence models"):
  with members:
    - "deep text to speech system with seq2seq model"
    - "transcoding compositionally using attention to find more generalizable solutions"
    - "on the convergence of adam and beyond"
    - "learning compositionally through attentive guidance"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "closed book training to improve summarization encoder 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.LG(Machine Learning)