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 machine translation with reconstruction"
  Abstract: Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy. It has been widely observed that NMT tends to repeatedly translate some source words while mistakenly ignoring other words. To alleviate this problem, we propose a novel encoder-decoder-reconstructor framework for NMT. The reconstructor, incorporated into the NMT model, manages to reconstruct the input source sentence from the hidden layer of the output target sentence, to ensure that the information in the source side is transformed to the target side as much as possible. Experiments show that the proposed framework significantly improves the adequacy of NMT output and achieves superior translation result over state-of-the-art NMT and statistical MT systems.

Connected through hyperedge HE-1 (induced by source paper "bi decoder augmented network for neural machine translation"):
  with members:
    - "reinforced dynamic reasoning for conversational question generation"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "macnet transferring knowledge from machine comprehension to sequence to sequence models"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "what do neural machine translation models learn about morphology"

Connected through hyperedge HE-2 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-3 (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-4 (induced by source paper "autoregressive text generation beyond feedback loops"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "large scale classification of structured objects using a crf with deep class embedding"
    - "deep state space models for unconditional word generation"
    - "language gans falling short"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "structured attention networks"
    - "advances in variational inference"

Connected through hyperedge HE-5 (induced by source paper "pseudolikelihood reranking with masked language models"):
  with members:
    - "passage re ranking with bert"
    - "effective sentence scoring method using bidirectional language model for speech recognition"
    - "pre trained language model representations for language generation"
    - "bertscore evaluating text generation with bert"
    - "transformer xl attentive language models beyond a fixed length context"
    - "language models with transformers"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "language modeling with deep transformers"

Connected through hyperedge HE-6 (induced by source paper "hierarchical photo scene encoder for album storytelling"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "hierarchically attentive rnn for album summarization and storytelling"

Connected through hyperedge HE-7 (induced by source paper "domain robustness in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "a call for clarity in reporting bleu scores"
    - "bi directional differentiable input reconstruction for low resource neural machine translation"
    - "attention is all you need"
    - "extending defensive distillation"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "revisiting challenges in data to text generation with fact grounding"):
  with members:
    - "learning to select track and generate for data to text"
    - "data to text generation with entity modeling"
    - "step by step separating planning from realization in neural data to text generation"
    - "end to end content and plan selection for data to text generation"
    - "describing a knowledge base"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "data to text generation with content selection and planning"
    - "learning to generate one sentence biographies from wikidata"

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)