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: "evaluating two stream cnn for video classification"
  Abstract: Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g.,"skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.

Connected through hyperedge HE-1 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs"):
  with members:
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "simple online and realtime tracking with a deep association metric"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "simple online and realtime tracking"
    - "fast zero shot image tagging"
    - "person re identification by discriminative selection in video ranking"
    - "deep residual learning for image recognition"
    - "zero shot learning via semantic similarity embedding"

Connected through hyperedge HE-2 (induced by source paper "vehicle re identification exploring feature fusion using multi stream convolutional networks"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "a two stream siamese neural network for vehicle re identification by using non overlapping cameras"):
  with members:
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-4 (induced by source paper "precise temporal action localization by evolving temporal proposals"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "non local neural networks"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-5 (induced by source paper "learning to score the figure skating sports videos"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "a structured self attentive sentence embedding"
    - "learning to score olympic events"
    - "real time action recognition with enhanced motion vector cnns"
    - "video summarization with long short term memory"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-6 (induced by source paper "learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks"):
  with members:
    - "learning discriminative features via label consistent neural network"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "understanding neural networks through deep visualization"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "beyond short snippets deep networks for video classification"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "learning discriminative features via label consistent neural network"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "beyond short snippets deep networks for video classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "towards good practices for very deep two stream convnets"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-8 (induced by source paper "fusing deep convolutional networks for large scale visual concept classification"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "deep residual learning for image recognition"
    - "towards good practices for very deep two stream convnets"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "two stream convolutional networks for action recognition in videos"

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)