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: "tracing a loose wordhood for chinese input method engine"
  Abstract: Chinese input methods are used to convert pinyin sequence or other Latin encoding systems into Chinese character sentences. For more effective pinyin-to-character conversion, typical Input Method Engines (IMEs) rely on a predefined vocabulary that demands manually maintenance on schedule. For the purpose of removing the inconvenient vocabulary setting, this work focuses on automatic wordhood acquisition by fully considering that Chinese inputting is a free human-computer interaction procedure. Instead of strictly defining words, a loose word likelihood is introduced for measuring how likely a character sequence can be a user-recognized word with respect to using IME. Then an online algorithm is proposed to adjust the word likelihood or generate new words by comparing user true choice for inputting and the algorithm prediction. The experimental results show that the proposed solution can agilely adapt to diverse typings and demonstrate performance approaching highly-optimized IME with fixed vocabulary.

Connected through hyperedge HE-1 (induced by source paper "a smart sliding chinese pinyin input method editor on touchscreen"):
  with members:
    - "open vocabulary learning for neural chinese pinyin ime"
    - "an actor critic algorithm for sequence prediction"
    - "effective approaches to attention based neural machine translation"
    - "continuous control with deep reinforcement learning"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "generating sequences with recurrent neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "open vocabulary learning for neural chinese pinyin ime"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "dynamic sentence sampling for efficient training of neural machine translation"
    - "lingke a fine grained multi turn chatbot for customer service"

Connected through hyperedge HE-3 (induced by source paper "neural based pinyin to character conversion with adaptive vocabulary"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "vocabulary selection strategies for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "chinese pinyin aided ime input what you have not keystroked yet"):
  with members:
    - "finding better subword segmentation for neural machine translation"
    - "deep enhanced representation for implicit discourse relation recognition"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "automatic article commenting the task and dataset"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "neural machine translation with extended context"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

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)