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: "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
  Abstract: Recent two-stream deep Convolutional Neural Networks (ConvNets) have made significant progress in recognizing human actions in videos. Despite their success, methods extending the basic two-stream ConvNet have not systematically explored possible network architectures to further exploit spatiotemporal dynamics within video sequences. Further, such networks often use different baseline two-stream networks. Therefore, the differences and the distinguishing factors between various methods using Recurrent Neural Networks (RNN) or convolutional networks on temporally-constructed feature vectors (Temporal-ConvNet) are unclear. In this work, we first demonstrate a strong baseline two-stream ConvNet using ResNet-101. We use this baseline to thoroughly examine the use of both RNNs and Temporal-ConvNets for extracting spatiotemporal information. Building upon our experimental results, we then propose and investigate two different networks to further integrate spatiotemporal information: 1) temporal segment RNN and 2) Inception-style Temporal-ConvNet. We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance. However, each of these methods require proper care to achieve state-of-the-art performance; for example, LSTMs require pre-segmented data or else they cannot fully exploit temporal information. Our analysis identifies specific limitations for each method that could form the basis of future work. Our experimental results on UCF101 and HMDB51 datasets achieve state-of-the-art performances, 94.1% and 69.0%, respectively, without requiring extensive temporal augmentation.

Connected through hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "multi fiber networks for video recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-2 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "temporal attentive alignment for large scale video domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "attending to discriminative certainty for domain adaptation"
    - "human action recognition and prediction a survey"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "multi adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "correlation net spatiotemporal multimodal deep learning for action recognition"):
  with members:
    - "outer product based neural collaborative filtering"
    - "neural collaborative filtering"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "follow the attention combining partial pose and object motion for fine grained action detection"):
  with members:
    - "skeleton based action recognition of people handling objects"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-5 (induced by source paper "temporal attentive alignment for large scale video domain adaptation"):
  with members:
    - "learning transferable self attentive representations for action recognition in untrimmed videos with weak supervision"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "learning correspondence from the cycle consistency of time"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "open set domain adaptation by backpropagation"
    - "a short note about kinetics 600"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "memory augmented temporal dynamic learning for action recognition"):
  with members:
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "motion appearance co memory networks for video question answering"
    - "learning dynamic memory networks for object tracking"
    - "memory warps for learning long term online video representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lattice long short term memory for human action recognition"
    - "attention clusters purely attention based local feature integration for video classification"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-7 (induced by source paper "representing videos based on scene layouts for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "c wsl count guided weakly supervised localization"

Connected through hyperedge HE-8 (induced by source paper "layout induced video representation for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local 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)