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: "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
  Abstract: Recognizing instances at different scales simultaneously is a fundamental challenge in visual detection problems. While spatial multi-scale modeling has been well studied in object detection, how to effectively apply a multi-scale architecture to temporal models for activity detection is still under-explored. In this paper, we identify three unique challenges that need to be specifically handled for temporal activity detection compared to its spatial counterpart. To address all these issues, we propose Dynamic Temporal Pyramid Network (DTPN), a new activity detection framework with a multi-scale pyramidal architecture featuring three novel designs: (1) We sample input video frames dynamically with varying frame per seconds (FPS) to construct a natural pyramidal input for video of an arbitrary length. (2) We design a two-branch multi-scale temporal feature hierarchy to deal with the inherent temporal scale variation of activity instances. (3) We further exploit the temporal context of activities by appropriately fusing multi-scale feature maps, and demonstrate that both local and global temporal contexts are important. By combining all these components into a uniform network, we end up with a single-shot activity detector involving single-pass inferencing and end-to-end training. Extensive experiments show that the proposed DTPN achieves state-of-the-art performance on the challenging ActvityNet dataset.

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for assessing physical rehabilitation exercises"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "temporal action detection with structured segment networks"
    - "deep representation learning for human motion prediction and classification"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "exercise motion classification from large scale wearable sensor data using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "man moment alignment network for natural language moment retrieval via iterative graph adjustment"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "graph r cnn for scene graph generation"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "multilevel language and vision integration for text to clip retrieval"
    - "tan temporal aggregation network for dense multi label action recognition"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "actor and action video segmentation from a sentence"

Connected through hyperedge HE-3 (induced by source paper "representing videos based on scene layouts for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "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"
    - "attend and interact higher order object interactions for video understanding"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "tan temporal aggregation network for dense multi label action recognition"):
  with members:
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "stereoscopic neural style transfer"
    - "deep motion boundary detection"
    - "decouple learning for parameterized image operators"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-6 (induced by source paper "3d feature pyramid attention module for robust visual speech recognition"):
  with members:
    - "contextual multi scale region convolutional 3d network for activity detection"
    - "temporal action detection with structured segment networks"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal 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)