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: "online open world recognition"
  Abstract: As we enter into the big data age and an avalanche of images have become readily available, recognition systems face the need to move from close, lab settings where the number of classes and training data are fixed, to dynamic scenarios where the number of categories to be recognized grows continuously over time, as well as new data providing useful information to update the system. Recent attempts, like the open world recognition framework, tried to inject dynamics into the system by detecting new unknown classes and adding them incrementally, while at the same time continuously updating the models for the known classes. incrementally adding new classes and detecting instances from unknown classes, while at the same time continuously updating the models for the known classes. In this paper we argue that to properly capture the intrinsic dynamic of open world recognition, it is necessary to add to these aspects (a) the incremental learning of the underlying metric, (b) the incremental estimate of confidence thresholds for the unknown classes, and (c) the use of local learning to precisely describe the space of classes. We extend three existing metric learning algorithms towards these goals by using online metric learning. Experimentally we validate our approach on two large-scale datasets in different learning scenarios. For all these scenarios our proposed methods outperform their non-online counterparts. We conclude that local and online learning is important to capture the full dynamics of open world recognition.

Connected through hyperedge HE-1 (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"
    - "open world learning and application to product classification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "knowledge is never enough towards web aided deep open world recognition"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "kitting in the wild through online domain adaptation"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "incremental learning for robot perception through hri"
    - "icarl incremental classifier and representation learning"
    - "identity mappings in deep residual networks"
    - "towards open world recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "open world learning and application to product classification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "kitting in the wild through online domain adaptation"):
  with members:
    - "best sources forward domain generalization through source specific nets"
    - "boosting domain adaptation by discovering latent domains"
    - "adaptive deep learning through visual domain localization"
    - "robust place categorization with deep domain generalization"
    - "deeper broader and artier domain generalization"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "icarl incremental classifier and representation learning"
    - "revisiting batch normalization for practical domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "unseen class discovery in open world classification"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "icarl incremental classifier and representation learning"
    - "a deep relevance matching model for ad hoc retrieval"
    - "deep image category discovery using a transferred similarity function"
    - "semi supervised vocabulary informed learning"
    - "towards open set deep networks"
    - "towards open world recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "open world visual recognition using knowledge graphs"):
  with members:
    - "zero shot learning the good the bad and the ugly"
    - "image embodied knowledge representation learning"
    - "learning hypergraph regularized attribute predictors"
    - "towards open world recognition"
    - "order embeddings of images and language"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "imagenet large scale visual recognition challenge"

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.CV(Computer Vision and Pattern Recognition)