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: "bayesian semantic instance segmentation in open set world"
  Abstract: This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of existing semantic instance segmentation methods requires annotation masks for all object instances, which is expensive to acquire or even infeasible in some realistic scenarios, where the number of categories may increase boundlessly. In this paper, we present a novel open-set semantic instance segmentation approach capable of segmenting all known and unknown object classes in images, based on the output of an object detector trained on known object classes. We formulate the problem using a Bayesian framework, where the posterior distribution is approximated with a simulated annealing optimization equipped with an efficient image partition sampler. We show empirically that our method is competitive with state-of-the-art supervised methods on known classes, but also performs well on unknown classes when compared with unsupervised methods.

Connected through hyperedge HE-1 (induced by source paper "4d generic video object proposals"):
  with members:
    - "mots multi object tracking and segmentation"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combined image and world space tracking in traffic scenes"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "unsupervised hard example mining from videos for improved object detection"
    - "maskrnn instance level video object segmentation"
    - "pcn point completion network"

Connected through hyperedge HE-2 (induced by source paper "identifying unknown instances for autonomous driving"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "large scale long tailed recognition in an open world"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "4d generic video object proposals"
    - "tensormask a foundation for dense object segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"

Connected through hyperedge HE-3 (induced by source paper "this is not what i imagined error detection for semantic segmentation through visual dissimilarity"):
  with members:
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "photographic image synthesis with cascaded refinement networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-4 (induced by source paper "efficacy of pixel level ood detection for semantic segmentation"):
  with members:
    - "cereals cost effective region based active learning for semantic segmentation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "discriminative out of distribution detection for semantic segmentation"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (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"
    - "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"

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)