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: "compact personalized models for neural machine translation"
  Abstract: We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture - combining a state-of-the-art self-attentive model with compact domain adaptation - provides high quality personalized machine translation that is both space and time efficient.

Connected through hyperedge HE-1 (induced by source paper "simple scalable adaptation for neural machine translation"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "non parametric adaptation for neural machine translation"
    - "massively multilingual neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "guiding variational response generator to exploit persona"):
  with members:
    - "learning personas from dialogue with attentive memory networks"
    - "training millions of personalized dialogue agents"
    - "extreme adaptation for personalized neural machine translation"
    - "steering output style and topic in neural response generation"
    - "a conditional variational framework for dialog generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"

Connected through hyperedge HE-3 (induced by source paper "interactive predictive neural machine translation through reinforcement and imitation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "fast policy learning through imitation and reinforcement"
    - "online learning for neural machine translation post editing"

Connected through hyperedge HE-4 (induced by source paper "incremental adaptation of nmt for professional post editors a user study"):
  with members:
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "online learning for effort reduction in interactive neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "a user study on online adaptation of neural machine translation to human post edits"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "demonstration of a neural machine translation system with online learning for translators"):
  with members:
    - "incremental adaptation of nmt for professional post editors a user study"
    - "achieving human parity on automatic chinese to english news translation"
    - "online learning for effort reduction in interactive neural machine translation"
    - "online learning for neural machine translation post editing"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)