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: "correcting length bias in neural machine translation"
  Abstract: We study two problems in neural machine translation (NMT). First, in beam search, whereas a wider beam should in principle help translation, it often hurts NMT. Second, NMT has a tendency to produce translations that are too short. Here, we argue that these problems are closely related and both rooted in label bias. We show that correcting the brevity problem almost eliminates the beam problem; we compare some commonly-used methods for doing this, finding that a simple per-word reward works well; and we introduce a simple and quick way to tune this reward using the perceptron algorithm.

Connected through hyperedge HE-1 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding"):
  with members:
    - "the curious case of neural text degeneration"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "hierarchical neural story generation"
    - "on the state of the art of evaluation in neural language models"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-2 (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-3 (induced by source paper "automatically extracting challenge sets for non local phenomena neural machine translation"):
  with members:
    - "star transformer"
    - "linguistic evaluation of german english machine translation using a test suite"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic neural machine translation using amr"
    - "semantic structural evaluation for text simplification"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"

Connected through hyperedge HE-4 (induced by source paper "controlling the output length of neural machine translation"):
  with members:
    - "positional encoding to control output sequence length"
    - "revisiting character based neural machine translation with capacity and compression"
    - "achieving human parity on automatic chinese to english news translation"
    - "multi task neural models for translating between styles within and across languages"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "scaling neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "speculative beam search for simultaneous translation"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "learning to stop in structured prediction for neural machine translation"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-6 (induced by source paper "learning to stop in structured prediction for neural machine translation"):
  with members:
    - "multi reference training with pseudo references for neural translation and text generation"
    - "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "group sparse cnns for question classification with answer sets"

Connected through hyperedge HE-7 (induced by source paper "on nmt search errors and model errors cat got your tongue"):
  with members:
    - "calibration of encoder decoder models for neural machine translation"
    - "the university of cambridge s machine translation systems for wmt18"
    - "why not be versatile applications of the sgnmt decoder for machine translation"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "analyzing neural mt search and model performance"
    - "attention is all you need"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"

Connected through hyperedge HE-8 (induced by source paper "unsupervised natural question answering with a small model"):
  with members:
    - "the curious case of neural text degeneration"
    - "learning and evaluating general linguistic intelligence"
    - "no training required exploring random encoders for sentence classification"
    - "non parametric adaptation for neural machine translation"
    - "a bert baseline for the natural questions"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to generate reviews and discovering sentiment"

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)