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: "exploiting unlabeled data in cnns by self supervised learning to rank"
  Abstract: For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50 percent.

Connected through hyperedge HE-1 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting"):
  with members:
    - "scar spatial channel wise attention regression networks for crowd counting"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "locality constrained spatial transformer network for video crowd counting"
    - "bayesian loss for crowd count estimation with point supervision"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "perspective guided convolution networks for crowd counting"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"

Connected through hyperedge HE-2 (induced by source paper "self supervised contextual bandits in computer vision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "self supervised exploration via disagreement"
    - "boosting few shot visual learning with self supervision"
    - "semi supervised learning with scarce annotations"
    - "deep clustering for unsupervised learning of visual features"
    - "two stream self supervised learning for action recognition"
    - "large scale study of curiosity driven learning"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-3 (induced by source paper "autoscale learning to scale for crowd counting"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "point in box out beyond counting persons in crowds"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "autofocus efficient multi scale inference"
    - "where are the blobs counting by localization with point supervision"
    - "context aware crowd counting"

Connected through hyperedge HE-4 (induced by source paper "pseudo labeling and confirmation bias in deep semi supervised learning"):
  with members:
    - "surprising effectiveness of few image unsupervised feature learning"
    - "unsupervised label noise modeling and loss correction"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-5 (induced by source paper "locate size and count accurately resolving people in dense crowds via detection"):
  with members:
    - "point in box out beyond counting persons in crowds"
    - "dafe fd density aware feature enrichment for face detection"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "switching convolutional neural network for crowd counting"

Connected through hyperedge HE-6 (induced by source paper "controlling biases and diversity in diverse image to image translation"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "women also snowboard overcoming bias in captioning models"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "weakly supervised domain specific color naming based on attention"

Connected through hyperedge HE-7 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion"):
  with members:
    - "direct modelling of speech emotion from raw speech"
    - "automated screening for distress a perspective for the future"
    - "attention augmented end to end multi task learning for emotion prediction from speech"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "flipped adversarial autoencoders"
    - "transfer learning for improving speech emotion classification accuracy"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "emotion recognition in speech using cross modal transfer in the wild"

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)