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 "only time can tell discovering temporal data for temporal modeling"):
  Abstract: Understanding temporal information and how the visual world changes over time is a fundamental ability of intelligent systems. In video understanding, temporal information is at the core of many current challenges, including compression, efficient inference, motion estimation or summarization. However, in current video datasets it has been observed that action classes can often be recognized without any temporal information from a single frame of video. As a result, both benchmarking and training in these datasets may give an unintentional advantage to models with strong image understanding capabilities, as opposed to those with strong temporal understanding. In this paper we address this problem head on by identifying action classes where temporal information is actually necessary to recognize them and call these "temporal classes". Selecting temporal classes using a computational method would bias the process. Instead, we propose a methodology based on a simple and effective human annotation experiment. We remove just the temporal information by shuffling frames in time and measure if the action can still be recognized. Classes that cannot be recognized when frames are not in order are included in the temporal Dataset. We observe that this set is statistically different from other static classes, and that performance in it correlates with a network's ability to capture temporal information. Thus we use it as a benchmark on current popular networks, which reveals a series of interesting facts. We also explore the effect of training on the temporal dataset, and observe that this leads to better generalization in unseen classes, demonstrating the need for more temporal data. We hope that the proposed dataset of temporal categories will help guide future research in temporal modeling for better video understanding.

Includes members:
  Member "scaling egocentric vision the epic kitchens dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to learn words from visual scenes")
  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 "a closer look at spatiotemporal convolutions for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "generalized rank pooling for activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "on the integration of optical flow and action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deformation flow based two stream network for lip reading")
  Member "from lifestyle vlogs to everyday interactions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial filters of dataset biases")
  Member "the kinetics human action video dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "ava a video dataset of spatio temporally localized atomic visual actions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatio temporal ranked attention networks for video captioning")

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)