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: "video classification with cnns using the codec as a spatio temporal activity sensor"
  Abstract: We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our approach begins with the observation that all modern video codecs divide the input frames into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a.k.a., MB texture decoding). This in turn allows for the derivation of spatio-temporal video activity regions at extremely high speed in comparison to conventional full-frame decoding followed by optical flow estimation. In order to evaluate the accuracy of a video classification framework based on such activity data, we independently train two CNN architectures on MB texture and MV correspondences and then fuse their scores to derive the final classification of each test video. Evaluation on two standard datasets shows that the proposed approach is competitive to the best two-stream video classification approaches found in the literature. At the same time: (i) a CPU-based realization of our MV extraction is over 977 times faster than GPU-based optical flow methods; (ii) selective decoding is up to 12 times faster than full-frame decoding; (iii) our proposed spatial and temporal CNNs perform inference at 5 to 49 times lower cloud computing cost than the fastest methods from the literature.

Connected through hyperedge HE-1 (induced by source paper "eva 2 exploiting temporal redundancy in live computer vision"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "flow guided feature aggregation for video object detection"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "reconfiguring the imaging pipeline for computer vision"
    - "exploring computation communication tradeoffs in camera systems"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-2 (induced by source paper "euphrates algorithm soc co design for low power mobile continuous vision"):
  with members:
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "reconfiguring the imaging pipeline for computer vision"
    - "exploring computation communication tradeoffs in camera systems"

Connected through hyperedge HE-3 (induced by source paper "rate accuracy trade off in video classification with deep convolutional neural networks"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "on the integration of optical flow and action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "attentional pooling for action recognition"
    - "real time action recognition with enhanced motion vector cnns"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-4 (induced by source paper "towards image understanding from deep compression without decoding"):
  with members:
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "learning convolutional networks for content weighted image compression"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimization of nonlinear transform codes for perceptual quality"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "understanding how image quality affects deep neural networks"
    - "on hyperspectral classification in the compressed domain"
    - "variable rate image compression with recurrent neural networks"

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)