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: "doc deep open classification of text documents"
  Abstract: Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.

Connected through hyperedge HE-1 (induced by source paper "conditional gaussian distribution learning for open set recognition"):
  with members:
    - "mnemonics training multi class incremental learning without forgetting"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "sparse representation based open set recognition"
    - "generative openmax for multi class open set classification"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "a post processing method for detecting unknown intent of dialogue system via pre trained deep neural network classifier"):
  with members:
    - "deep unknown intent detection with margin loss"
    - "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"
    - "open category classification by adversarial sample generation"
    - "sequential short text classification with recurrent and convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "transferable multi domain state generator for task oriented dialogue systems"):
  with members:
    - "global to local memory pointer networks for task oriented dialogue"
    - "the natural language decathlon multitask learning as question answering"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "neural approaches to conversational ai"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"

Connected through hyperedge HE-4 (induced by source paper "deep cnn based multi task learning for open set recognition"):
  with members:
    - "learning deep features for one class classification"
    - "open category detection with pac guarantees"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "and autoregressive novelty detectors"
    - "learning from noisy web data with category level supervision"
    - "classification reconstruction learning for open set recognition"
    - "the extreme value machine"

Connected through hyperedge HE-5 (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"
    - "efficient large scale domain classification with personalized attention"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "dafe fd density aware feature enrichment for face detection"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-7 (induced by source paper "open world learning and application to product classification"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "efficient large scale domain classification with personalized attention"
    - "learning to teach"
    - "probabilistic model agnostic meta learning"
    - "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-8 (induced by source paper "are out of distribution detection methods effective on large scale datasets"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep cnn based multi task learning for open set recognition"
    - "remind your neural network to prevent catastrophic forgetting"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "reducing network agnostophobia"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "memory efficient experience replay for streaming learning"

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)