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: "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
  Abstract: Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale. In this paper, we propose a set of efficient and scalable neural shortlisting-reranking models for large-scale domain classification in IPDAs. The shortlisting stage focuses on efficiently trimming all domains down to a list of k-best candidate domains, and the reranking stage performs a list-wise reranking of the initial k-best domains with additional contextual information. We show the effectiveness of our approach with extensive experiments on 1,500 IPDA domains.

Connected through hyperedge HE-1 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "efficient large scale domain classification with personalized attention"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "classification from positive unlabeled and biased negative data"
    - "supervised domain enablement attention for personalized domain classification"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "continuous learning for large scale personalized domain classification"):
  with members:
    - "efficient large scale domain classification with personalized attention"
    - "self normalizing neural networks"
    - "continual learning through synaptic intelligence"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "measuring catastrophic forgetting in neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "icarl incremental classifier and representation learning"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a bandit approach to posterior dialog orchestration under a budget"):
  with members:
    - "context attentive bandits contextual bandit with restricted context"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "coupled representation learning for domains intents and slots in spoken language understanding"):
  with members:
    - "efficient large scale domain classification with personalized attention"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "deep contextualized word representations"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "speaker sensitive dual memory networks for multi turn slot tagging"
    - "semi supervised classification with graph convolutional networks"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "distributed representations of words and phrases and their compositionality"

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)