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 video classification using fewer frames"
  Abstract: Recently,there has been a lot of interest in building compact models for video classification which have a small memory footprint (<1 GB). While these models are compact, they typically operate by repeated application of a small weight matrix to all the frames in a video. E.g. recurrent neural network based methods compute a hidden state for every frame of the video using a recurrent weight matrix. Similarly, cluster-and-aggregate based methods such as NetVLAD, have a learnable clustering matrix which is used to assign soft-clusters to every frame in the video. Since these models look at every frame in the video, the number of floating point operations (FLOPs) is still large even though the memory footprint is small. We focus on building compute-efficient video classification models which process fewer frames and hence have less number of FLOPs. Similar to memory efficient models, we use the idea of distillation albeit in a different setting. Specifically, in our case, a compute-heavy teacher which looks at all the frames in the video is used to train a compute-efficient student which looks at only a small fraction of frames in the video. This is in contrast to a typical memory efficient Teacher-Student setting, wherein both the teacher and the student look at all the frames in the video but the student has fewer parameters. Our work thus complements the research on memory efficient video classification. We do an extensive evaluation with three types of models for video classification,viz.(i) recurrent models (ii) cluster-and-aggregate models and (iii) memory-efficient cluster-and-aggregate models and show that in each of these cases, a see-it-all teacher can be used to train a compute efficient see-very-little student. We show that the proposed student network can reduce the inference time by 30% and the number of FLOPs by approximately 90% with a negligible drop in the performance.

Connected through hyperedge HE-1 (induced by source paper "faster recurrent networks for efficient video classification"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "adaframe adaptive frame selection for fast video recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "hallucinet ing spatiotemporal representations using 2d cnn"):
  with members:
    - "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "temporal sequence distillation towards few frame action recognition in videos"
    - "motion feature network fixed motion filter for action recognition"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "slowfast networks for video recognition"
    - "the activitynet large scale activity recognition challenge 2018 summary"

Connected through hyperedge HE-3 (induced by source paper "extreme low resolution activity recognition with spatial temporal attention transfer"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"

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)