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 cnn based multi task learning for open set recognition"
  Abstract: We propose a novel deep convolutional neural network (CNN) based multi-task learning approach for open-set visual recognition. We combine a classifier network and a decoder network with a shared feature extractor network within a multi-task learning framework. We show that this approach results in better open-set recognition accuracy. In our approach, reconstruction errors from the decoder network are utilized for open-set rejection. In addition, we model the tail of the reconstruction error distribution from the known classes using the statistical Extreme Value Theory to improve the overall performance. Experiments on multiple image classification datasets are performed and it is shown that this method can perform significantly better than many competitive open set recognition algorithms available in the literature. The code will be made available at: this http URL.

Connected through hyperedge HE-1 (induced by source paper "attention aware age agnostic visual place recognition"):
  with members:
    - "discriminative label consistent domain adaptation"
    - "open set domain adaptation by backpropagation"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "attention based ensemble for deep metric learning"
    - "panorama to panorama matching for location recognition"

Connected through hyperedge HE-2 (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"
    - "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"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-3 (induced by source paper "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"):
  with members:
    - "deep transfer learning for multiple class novelty detection"
    - "deep multimodal subspace clustering networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "correlation alignment for unsupervised domain adaptation"
    - "deep coral correlation alignment for deep domain adaptation"

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)