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: "efficient large scale video classification"
  Abstract: Video classification has advanced tremendously over the recent years. A large part of the improvements in video classification had to do with the work done by the image classification community and the use of deep convolutional networks (CNNs) which produce competitive results with hand- crafted motion features. These networks were adapted to use video frames in various ways and have yielded state of the art classification results. We present two methods that build on this work, and scale it up to work with millions of videos and hundreds of thousands of classes while maintaining a low computational cost. In the context of large scale video processing, training CNNs on video frames is extremely time consuming, due to the large number of frames involved. We propose to avoid this problem by training CNNs on either YouTube thumbnails or Flickr images, and then using these networks' outputs as features for other higher level classifiers. We discuss the challenges of achieving this and propose two models for frame-level and video-level classification. The first is a highly efficient mixture of experts while the latter is based on long short term memory neural networks. We present results on the Sports-1M video dataset (1 million videos, 487 classes) and on a new dataset which has 12 million videos and 150,000 labels.

Connected through hyperedge HE-1 (induced by source paper "sketch2code generating a website from a paper mockup"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "pix2code generating code from a graphical user interface screenshot"
    - "richer convolutional features for edge detection"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "neural language modeling with visual features"):
  with members:
    - "visually grounded situated learning in neural models"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "towards automatic learning of procedures from web instructional videos"
    - "localizing moments in video with natural language"
    - "the something something video database for learning and evaluating visual common sense"
    - "grounding of textual phrases in images by reconstruction"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-3 (induced by source paper "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"):
  with members:
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "security evaluation of pattern classifiers under attack"
    - "deep learning for video classification and captioning"
    - "practical black box attacks against machine learning"
    - "concrete problems in ai safety"
    - "stealing machine learning models via prediction apis"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-4 (induced by source paper "deep local video feature for action recognition"):
  with members:
    - "deep quantization encoding convolutional activations with deep generative model"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep temporal linear encoding networks"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "towards good practices for very deep two stream convnets"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"

Connected through hyperedge HE-5 (induced by source paper "data dependent generalization bounds for multi class classification"):
  with members:
    - "generalization error bounds for learning to rank does the length of document lists matter"
    - "a vector contraction inequality for rademacher complexities"
    - "lshtc a benchmark for large scale text classification"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "conditional probability tree estimation analysis and algorithms"
    - "maximum margin multiclass nearest neighbors"
    - "multiclass learnability and the erm principle"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-6 (induced by source paper "handcrafted local features are convolutional neural networks"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "unsupervised learning of video representations using lstms"
    - "beyond short snippets deep networks for video classification"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "the best of both worlds combining data independent and data driven approaches for action recognition"
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "feature sampling and partitioning for visual vocabulary generation on large action classification datasets"

Connected through hyperedge HE-7 (induced by source paper "what objective does self paced learning indeed optimize"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "on the global convergence of majorization minimization algorithms for nonconvex optimization problems"
    - "relaxed sparse eigenvalue conditions for sparse estimation via non convex regularized regression"
    - "are all training examples equally valuable"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"

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)