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: "efficient large scale domain classification with personalized attention"
  Abstract: In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs). This scenario is observed for many mainstream IPDAs in industry that allow third parties to develop thousands of new domains to augment built-in ones to rapidly increase domain coverage and overall IPDA capabilities. We propose a scalable neural model architecture with a shared encoder, a novel attention mechanism that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. Our architecture is designed to efficiently accommodate new domains that appear in-between full model retraining cycles with a rapid bootstrapping mechanism two orders of magnitude faster than retraining. We account for practical constraints in real-time production systems, and design to minimize memory footprint and runtime latency. We demonstrate that incorporating personalization results in significantly more accurate domain classification in the setting with thousands of overlapping 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"
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "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 "out of domain detection for natural language understanding in dialog systems"):
  with members:
    - "open world learning and application to product classification"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "learning from positive and unlabeled data a survey"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"

Connected through hyperedge HE-3 (induced by source paper "open world learning and application to product classification"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "learning to teach"
    - "probabilistic model agnostic meta learning"
    - "doc deep open classification of text documents"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "towards personalized dialog policies for conversational skill discovery"):
  with members:
    - "the technological gap between virtual assistants and recommendation systems"
    - "continuous learning for large scale personalized domain classification"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "attentive memory networks efficient machine reading for conversational search"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "learning robust dialog policies in noisy environments"
    - "improving search through a3c reinforcement learning based conversational agent"
    - "simpleds a simple deep reinforcement learning dialogue system"

Connected through hyperedge HE-5 (induced by source paper "sumbt slot utterance matching for universal and scalable belief tracking"):
  with members:
    - "fully statistical neural belief tracking"
    - "towards universal dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "flexible and scalable state tracking framework for goal oriented dialogue systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"

Connected through hyperedge HE-6 (induced by source paper "large scale question paraphrase retrieval with smoothed deep metric learning"):
  with members:
    - "sampling matters in deep embedding learning"
    - "billion scale similarity search with gpus"
    - "comparative study of cnn and rnn for natural language processing"
    - "bilateral multi perspective matching for natural language sentences"
    - "neural models for information retrieval"
    - "a decomposable attention model for natural language inference"
    - "smart reply automated response suggestion for email"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "continuous learning for large scale personalized domain classification"):
  with members:
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "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-8 (induced by source paper "coupled representation learning for domains intents and slots in spoken language understanding"):
  with members:
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "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)