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: "learning to learn from noisy web videos"
  Abstract: Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training videos is feasible for some action classes but doesn't scale to the full long-tailed distribution of actions. A promising way to address this is to leverage noisy data from web queries to learn new actions, using semi-supervised or "webly-supervised" approaches. However, these methods typically do not learn domain-specific knowledge, or rely on iterative hand-tuned data labeling policies. In this work, we instead propose a reinforcement learning-based formulation for selecting the right examples for training a classifier from noisy web search results. Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts. Experiments on the challenging Sports-1M action recognition benchmark as well as on additional fine-grained and newly emerging action classes demonstrate that our method is able to learn good labeling policies for noisy data and use this to learn accurate visual concept classifiers.

Connected through hyperedge HE-1 (induced by source paper "transferring cross domain knowledge for video sign language recognition"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "word level deep sign language recognition from video a new large scale dataset and methods comparison"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "ms asl a large scale data set and benchmark for understanding american sign language"
    - "neural sign language translation based on human keypoint estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "prototypical networks for few shot learning"
    - "attend to you personalized image captioning with context sequence memory networks"

Connected through hyperedge HE-2 (induced by source paper "mining youtube a dataset for learning fine grained action concepts from webly supervised video data"):
  with members:
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "look listen and learn"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "towards automatic learning of procedures from web instructional videos"

Connected through hyperedge HE-3 (induced by source paper "piecewise classifier mappings learning fine grained learners for novel categories with few examples"):
  with members:
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "learning feed forward one shot learners"

Connected through hyperedge HE-4 (induced by source paper "imitation learning from imperfect demonstration"):
  with members:
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "positive unlabeled learning with non negative risk estimator"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "a deep reinforcement learning chatbot"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "tracking as online decision making learning a policy from streaming videos with reinforcement learning"
    - "learning motion patterns in videos"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-5 (induced by source paper "a free lunch in generating datasets building a vqg and vqa system with attention and humans in the loop"):
  with members:
    - "attention is all you need"
    - "analyzing the behavior of visual question answering models"
    - "generating natural questions about an image"
    - "automatic generation of grounded visual questions"
    - "visual question answering a survey of methods and datasets"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "domain adversarial reinforcement learning for partial domain adaptation"):
  with members:
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "partial transfer learning with selective adversarial networks"
    - "video captioning via hierarchical reinforcement learning"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "a self adaptive proposal model for temporal action detection based on reinforcement learning"
    - "unsupervised domain adaptation with residual transfer networks"

Connected through hyperedge HE-7 (induced by source paper "reinforced co training"):
  with members:
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "clickbait detection in tweets using self attentive network"
    - "deep reinforcement learning that matters"
    - "learning to identify ambiguous and misleading news headlines"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "neural architecture search with reinforcement learning"
    - "efficient character level document classification by combining convolution and recurrent layers"

Connected through hyperedge HE-8 (induced by source paper "learning sampling policies for domain adaptation"):
  with members:
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "adversarial discriminative domain adaptation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "dueling network architectures for deep reinforcement learning"
    - "adam a method for stochastic optimization"

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)