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: "low rank rnn adaptation for context aware language modeling"
  Abstract: A context-aware language model uses location, user and/or domain metadata (context) to adapt its predictions. In neural language models, context information is typically represented as an embedding and it is given to the RNN as an additional input, which has been shown to be useful in many applications. We introduce a more powerful mechanism for using context to adapt an RNN by letting the context vector control a low-rank transformation of the recurrent layer weight matrix. Experiments show that allowing a greater fraction of the model parameters to be adjusted has benefits in terms of perplexity and classification for several different types of context.

Connected through hyperedge HE-1 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "topic guided variational autoencoders for text generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"

Connected through hyperedge HE-2 (induced by source paper "personalized dialogue generation with diversified traits"):
  with members:
    - "training millions of personalized dialogue agents"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "personalization in goal oriented dialog"

Connected through hyperedge HE-3 (induced by source paper "rethinking attribute representation and injection for sentiment classification"):
  with members:
    - "categorical metadata representation for customized text classification"
    - "dual memory network model for biased product review classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "translations as additional contexts for sentence classification"
    - "improving review representations with user attention and product attention for sentiment classification"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "cold start aware user and product attention for sentiment classification"
    - "extreme adaptation for personalized neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "reverse transfer learning can word embeddings trained for different nlp tasks improve neural language models"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "relevance based word embedding"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-5 (induced by source paper "continual adaptation for efficient machine communication"):
  with members:
    - "a neural model of adaptation in reading"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "personalized language model for query auto completion"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "context aware captions from context agnostic supervision"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-6 (induced by source paper "visual natural language query auto completion for estimating instance probabilities"):
  with members:
    - "personalized language model for query auto completion"
    - "learning to segment every thing"
    - "segmentation from natural language expressions"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "natural language object retrieval"

Connected through hyperedge HE-7 (induced by source paper "personalized language model for query auto completion"):
  with members:
    - "on the state of the art of evaluation in neural language models"
    - "dynamic evaluation of neural sequence models"
    - "a persona based neural conversation model"
    - "adam a method for stochastic optimization"

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)