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: "domain adaptation of recurrent neural networks for natural language understanding"
  Abstract: The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks. The approach supports an open vocabulary, which allows the models to generalize to unseen words, which is particularly important when very little training data is used. A newly collected crowd-sourced data set, covering four different domains, is used to demonstrate the effectiveness of the domain adaptation and open vocabulary techniques.

Connected through hyperedge HE-1 (induced by source paper "semi supervised representation learning via dual autoencoders for domain adaptation"):
  with members:
    - "a survey on deep transfer learning"
    - "partial adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "domain adaptation for ear recognition using deep convolutional neural networks"
    - "an extended framework for marginalized domain adaptation"
    - "conditional adversarial domain adaptation"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"

Connected through hyperedge HE-3 (induced by source paper "bootstrapping nlu models with multi task learning"):
  with members:
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "learning word vectors for 157 languages"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "from characters to words to in between do we capture morphology"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-4 (induced by source paper "efficient large scale domain classification with personalized attention"):
  with members:
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "self normalizing neural networks"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "transfer learning for neural semantic parsing"
    - "bag of tricks for efficient text classification"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "evolutionary data measures understanding the difficulty of text classification tasks"):
  with members:
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "word embedding perturbation for sentence classification"
    - "learning to generate reviews and discovering sentiment"
    - "automated hate speech detection and the problem of offensive language"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"

Connected through hyperedge HE-6 (induced by source paper "submodularity inspired data selection for goal oriented chatbot training based on sentence embeddings"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "neural models for sequence chunking"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "elastic crfs for open ontology slot filling"):
  with members:
    - "zero shot dialog generation with cross domain latent actions"
    - "scalable multi domain dialogue state tracking"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "structured prediction energy networks"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "zero shot adaptive transfer for conversational language understanding"):
  with members:
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "end to end neural coreference resolution"
    - "zero shot transfer learning for event extraction"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

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)