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: "stm spatiotemporal and motion encoding for action recognition"
  Abstract: Spatiotemporal and motion features are two complementary and crucial information for video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn spatiotemporal features and another flow stream to learn motion features. In this work, we aim to efficiently encode these two features in a unified 2D framework. To this end, we first propose an STM block, which contains a Channel-wise SpatioTemporal Module (CSTM) to present the spatiotemporal features and a Channel-wise Motion Module (CMM) to efficiently encode motion features. We then replace original residual blocks in the ResNet architecture with STM blcoks to form a simple yet effective STM network by introducing very limited extra computation cost. Extensive experiments demonstrate that the proposed STM network outperforms the state-of-the-art methods on both temporal-related datasets (i.e., Something-Something v1 & v2 and Jester) and scene-related datasets (i.e., Kinetics-400, UCF-101, and HMDB-51) with the help of encoding spatiotemporal and motion features together.

Connected through hyperedge HE-1 (induced by source paper "multi modal domain adaptation for fine grained action recognition"):
  with members:
    - "temporal attentive alignment for large scale video domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "unsupervised domain adaptation through self supervision"
    - "adversarial cross domain action recognition with co attention"
    - "scaling egocentric vision the epic kitchens dataset"
    - "slowfast networks for video recognition"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-2 (induced by source paper "sth spatio temporal hybrid convolution for efficient action recognition"):
  with members:
    - "dynamonet dynamic action and motion network"
    - "learning spatio temporal representation with local and global diffusion"
    - "eco efficient convolutional network for online video understanding"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "motion feature network fixed motion filter for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-3 (induced by source paper "ctm collaborative temporal modeling for action recognition"):
  with members:
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "a short note about kinetics 600"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-4 (induced by source paper "gate shift networks for video action recognition"):
  with members:
    - "attentive spatio temporal representation learning for diving classification"
    - "video modeling with correlation networks"
    - "action recognition with spatial temporal discriminative filter banks"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "grouped spatial temporal aggregation for efficient action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "eco efficient convolutional network for online video understanding"

Connected through hyperedge HE-5 (induced by source paper "adversarial pyramid network for video domain generalization"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "feature critic networks for heterogeneous domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "spatiotemporal pyramid network for video action recognition"
    - "domain generalization via model agnostic learning of semantic features"
    - "generalizing across domains via cross gradient training"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-6 (induced by source paper "a multigrid method for efficiently training video models"):
  with members:
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "grouped spatial temporal aggregation for efficient action recognition"
    - "long term feature banks for detailed video understanding"
    - "evolving space time neural architectures for videos"
    - "video action transformer network"
    - "neural rejuvenation improving deep network training by enhancing computational resource utilization"
    - "slowfast networks for video recognition"

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)