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: "open world learning and application to product classification"
  Abstract: Classic supervised learning makes the closed-world assumption that the classes seen in testing must have appeared in training. However, this assumption is often violated in real-world applications. For example, in a social media site, new topics emerge constantly and in e-commerce, new categories of products appear daily. A model that cannot detect new/unseen topics or products is hard to function well in such open environments. A desirable model working in such environments must be able to (1) reject examples from unseen classes (not appeared in training) and (2) incrementally learn the new/unseen classes to expand the existing model. This is called open-world learning (OWL). This paper proposes a new OWL method based on meta-learning. The key novelty is that the model maintains only a dynamic set of seen classes that allows new classes to be added or deleted with no need for model re-training. Each class is represented by a small set of training examples. In testing, the meta-classifier only uses the examples of the maintained seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results with e-commerce product classification show that the proposed method is highly effective1.

Connected through hyperedge HE-1 (induced by source paper "out of domain detection for natural language understanding in dialog systems"):
  with members:
    - "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"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"

Connected through hyperedge HE-2 (induced by source paper "multi stage deep classifier cascades for open world recognition"):
  with members:
    - "robust regression via online feature selection under adversarial data corruption"
    - "learning deep features for one class classification"
    - "random warping series a random features method for time series embedding"
    - "progressive neural networks"
    - "open world visual recognition using knowledge graphs"
    - "similarity preserving representation learning for time series clustering"
    - "icarl incremental classifier and representation learning"
    - "online open world recognition"

Connected through hyperedge HE-3 (induced by source paper "continual rare class recognition with emerging novel subclasses"):
  with members:
    - "unseen class discovery in open world classification"
    - "doc deep open classification of text documents"
    - "continual learning with deep generative replay"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "fearnet brain inspired model for incremental learning"
    - "icarl incremental classifier and representation learning"
    - "classification under streaming emerging new classes a solution using completely random trees"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "large scale long tailed recognition in an open world"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "bayesian semantic instance segmentation in open set world"
    - "learning a neural network based representation for open set recognition"
    - "open category detection with pac guarantees"
    - "open set domain adaptation by backpropagation"
    - "reducing network agnostophobia"
    - "learning factorized representations for open set domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "collective decision for open set recognition"):
  with members:
    - "learning a neural network based representation for open set recognition"
    - "unseen class discovery in open world classification"
    - "the extreme value machine"
    - "doc deep open classification of text documents"
    - "adversarial robustness softmax versus openmax"
    - "sparse representation based open set recognition"
    - "generative openmax for multi class open set classification"
    - "towards open set deep networks"

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)