human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "if ttn information fused temporal transformation network for video action recognition"):
  Abstract: Effective spatiotemporal feature representation is crucial to the video-based action recognition task. Focusing on discriminate spatiotemporal feature learning, we propose Information Fused Temporal Transformation Network (IF-TTN) for action recognition on top of popular Temporal Segment Network (TSN) framework. In the network, Information Fusion Module (IFM) is designed to fuse the appearance and motion features at multiple ConvNet levels for each video snippet, forming a short-term video descriptor. With fused features as inputs, Temporal Transformation Networks (TTN) are employed to model middle-term temporal transformation between the neighboring snippets following a sequential order. As TSN itself depicts long-term temporal structure by segmental consensus, the proposed network comprehensively considers multiple granularity temporal features. Our IF-TTN achieves the state-of-the-art results on two most popular action recognition datasets: UCF101 and HMDB51. Empirical investigation reveals that our architecture is robust to the input motion map quality. Replacing optical flow with the motion vectors from compressed video stream, the performance is still comparable to the flow-based methods while the testing speed is 10x faster.

Includes members:
  Member "spatiotemporal pyramid network for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "compressed video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motion excited sampler video adversarial attack with sparked prior")
  Member "lattice long short term memory for human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive deep metric embeddings for person re identification under occlusions")
  Member "ava a video dataset of spatio temporally localized atomic visual actions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "convnet architecture search for spatiotemporal feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamonet dynamic action and motion network")
  Member "optical flow guided feature a fast and robust motion representation for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "v4d 4d convolutional neural networks for video level representation learning")
  Member "temporal relational reasoning in videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")

Question: Please tell me which class the source paper 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)