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: "adapting models to signal degradation using distillation"
  Abstract: Model compression and knowledge distillation have been successfully applied for cross-architecture and cross-domain transfer learning. However, a key requirement is that training examples are in correspondence across the domains. We show that in many scenarios of practical importance such aligned data can be synthetically generated using computer graphics pipelines allowing domain adaptation through distillation. We apply this technique to learn models for recognizing low-resolution images using labeled high-resolution images, non-localized objects using labeled localized objects, line-drawings using labeled color images, etc. Experiments on various fine-grained recognition datasets demonstrate that the technique improves recognition performance on the low-quality data and beats strong baselines for domain adaptation. Finally, we present insights into workings of the technique through visualizations and relating it to existing literature.

Connected through hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "when does self supervision improve few shot learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "domain generalization by solving jigsaw puzzles"

Connected through hyperedge HE-3 (induced by source paper "bringing giant neural networks down to earth with unlabeled data"):
  with members:
    - "importance estimation for neural network pruning"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "deep predictive coding network for object recognition"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "exploring the limits of weakly supervised pretraining"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-4 (induced by source paper "boosting supervision with self supervision for few shot learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "revisiting self supervised visual representation learning"
    - "attentive single tasking of multiple tasks"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-5 (induced by source paper "tkd temporal knowledge distillation for active perception"):
  with members:
    - "a novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga"
    - "real world anomaly detection in surveillance videos"
    - "object detection at 200 frames per second"
    - "yolov3 an incremental improvement"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "online model distillation for efficient video inference"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-6 (induced by source paper "low resolution face recognition in the wild via selective knowledge distillation"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "low resolution face recognition in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"
    - "data distillation towards omni supervised learning"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"

Connected through hyperedge HE-7 (induced by source paper "towards fairness in visual recognition effective strategies for bias mitigation"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "on fairness and calibration"
    - "what actions are needed for understanding human actions in videos"

Connected through hyperedge HE-8 (induced by source paper "learning metrics from teachers compact networks for image embedding"):
  with members:
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "metric learning for novelty and anomaly detection"
    - "learning deep representations with probabilistic knowledge transfer"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "rankiqa learning from rankings for no reference image quality assessment"

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)