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: "deep unknown intent detection with margin loss"
  Abstract: Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.

Connected through hyperedge HE-1 (induced by source paper "a post processing method for detecting unknown intent of dialogue system via pre trained deep neural network classifier"):
  with members:
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "doc deep open classification of text documents"
    - "open category classification by adversarial sample generation"
    - "sequential short text classification with recurrent and convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "discovering new intents via constrained deep adaptive clustering with cluster refinement"):
  with members:
    - "train one get one free partially supervised neural network for bug report duplicate detection and clustering"
    - "huggingface s transformers state of the art natural language processing"
    - "deep clustering for unsupervised learning of visual features"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "prototypical networks for few shot learning"
    - "learning to cluster in order to transfer across domains and tasks"
    - "semi supervised clustering for short text via deep representation learning"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-3 (induced by source paper "likelihood ratios and generative classifiers for unsupervised out of domain detection in task oriented dialog"):
  with members:
    - "deep anomaly detection with outlier exposure"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

Connected through hyperedge HE-4 (induced by source paper "an evaluation dataset for intent classification and out of scope prediction"):
  with members:
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "benchmarking natural language understanding services for building conversational agents"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "bag of tricks for efficient text 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)