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: "towards open world recognition"
  Abstract: With the of advent rich classification models and high computational power visual recognition systems have found many operational applications. Recognition in the real world poses multiple challenges that are not apparent in controlled lab environments. The datasets are dynamic and novel categories must be continuously detected and then added. At prediction time, a trained system has to deal with myriad unseen categories. Operational systems require minimal downtime, even to learn. To handle these operational issues, we present the problem of Open World Recognition and formally define it. We prove that thresholding sums of monotonically decreasing functions of distances in linearly transformed feature space can balance “open space risk” and empirical risk. Our theory extends existing algorithms for open world recognition. We present a protocol for evaluation of open world recognition systems. We present the Nearest Non-Outlier (NNO) algorithm that evolves model efficiently, adding object categories incrementally while detecting outliers and managing open space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to validate the effectiveness of our method on large scale visual recognition tasks. NNO consistently yields superior results on open world recognition.

Connected through hyperedge HE-1 (induced by source paper "crl class representative learning for image classification"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "meta transfer networks for zero shot learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards universal representation for unseen action recognition"
    - "complementary attributes a new clue to zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "semi supervised class discovery"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "continual unsupervised representation learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "an evaluation dataset for intent classification and out of scope prediction"
    - "revisiting self supervised visual representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "co teaching robust training of deep neural networks with extremely noisy labels"

Connected through hyperedge HE-3 (induced by source paper "generalized odin detecting out of distribution image without learning from out of distribution data"):
  with members:
    - "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"
    - "moment matching for multi source domain adaptation"
    - "reducing network agnostophobia"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "metric learning for novelty and anomaly detection"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-4 (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"
    - "doc deep open classification of text documents"
    - "sparse representation based open set recognition"
    - "generative openmax for multi class open set classification"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "what can robotics research learn from computer vision research"):
  with members:
    - "a mask rcnn baseline for probabilistic object detection"
    - "do imagenet classifiers generalize to imagenet"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "orb slam a versatile and accurate monocular slam system"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "towards open set semantic segmentation of aerial images"):
  with members:
    - "dynamic multicontext segmentation of remote sensing images based on convolutional networks"
    - "densely connected convolutional networks"
    - "towards open set deep networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "moving towards open set incremental learning readily discovering new authors"):
  with members:
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "icarl incremental classifier and representation learning"
    - "towards open set deep networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

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)