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: "on the integration of optical flow and action recognition"
  Abstract: Most of the top performing action recognition methods use optical flow as a "black box" input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformations to better understand how these affect a state-of-the-art action recognition method. Furthermore, we fine tune two neural-network flow methods end-to-end on the most widely used action recognition dataset (UCF101). Based on these experiments, we make the following five observations: 1) optical flow is useful for action recognition because it is invariant to appearance, 2) optical flow methods are optimized to minimize end-point-error (EPE), but the EPE of current methods is not well correlated with action recognition performance, 3) for the flow methods tested, accuracy at boundaries and at small displacements is most correlated with action recognition performance, 4) training optical flow to minimize classification error instead of minimizing EPE improves recognition performance, and 5) optical flow learned for the task of action recognition differs from traditional optical flow especially inside the human body and at the boundary of the body. These observations may encourage optical flow researchers to look beyond EPE as a goal and guide action recognition researchers to seek better motion cues, leading to a tighter integration of the optical flow and action recognition communities.

Connected through hyperedge HE-1 (induced by source paper "deformation flow based two stream network for lip reading"):
  with members:
    - "learning spatio temporal features with two stream deep 3d cnns for lipreading"
    - "multi grained spatio temporal modeling for lip reading"
    - "self supervised learning of a facial attribute embedding from video"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-2 (induced by source paper "dmc net generating discriminative motion cues for fast compressed video action recognition"):
  with members:
    - "end to end learning of motion representation for video understanding"
    - "multi fiber networks for video recognition"
    - "contextual multi scale region convolutional 3d network for activity detection"
    - "stochastic adversarial video prediction"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "mimetics towards understanding human actions out of context"):
  with members:
    - "repair removing representation bias by dataset resampling"
    - "star net action recognition using spatio temporal activation reprojection"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "action machine rethinking action recognition in trimmed videos"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "pose based two stream relational networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "two stream video classification with cross modality attention"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dual attention network for scene segmentation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "delving into 3d action anticipation from streaming videos"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "temporal action detection with structured segment networks"
    - "encouraging lstms to anticipate actions very early"
    - "a new representation of skeleton sequences for 3d action recognition"

Connected through hyperedge HE-6 (induced by source paper "flow distilled ip two stream networks for compressed video action recognition"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "representation flow for action recognition"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-7 (induced by source paper "predictive coding networks meet action recognition"):
  with members:
    - "video representation learning by dense predictive coding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "end to end video level representation learning for action recognition"
    - "feature generating networks for zero shot learning"
    - "deep predictive coding networks for video prediction and unsupervised learning"

Connected through hyperedge HE-8 (induced by source paper "only time can tell discovering temporal data for temporal modeling"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "generalized rank pooling for activity recognition"
    - "from lifestyle vlogs to everyday interactions"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "rank pooling for action 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)