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: "convnet architecture search for spatiotemporal feature learning"
  Abstract: Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning. Although any image representation can be applied to video frames, a dedicated spatiotemporal representation is still vital in order to incorporate motion patterns that cannot be captured by appearance based models alone. This paper presents an empirical ConvNet architecture search for spatiotemporal feature learning, culminating in a deep 3-dimensional (3D) Residual ConvNet. Our proposed architecture outperforms C3D by a good margin on Sports-1M, UCF101, HMDB51, THUMOS14, and ASLAN while being 2 times faster at inference time, 2 times smaller in model size, and having a more compact representation.

Connected through hyperedge HE-1 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "self supervised learning of face representations for video face clustering"
    - "learning human optical flow"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "distinit learning video representations without a single labeled video"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "taskonomy disentangling task transfer learning"
    - "a better baseline for ava"
    - "video action transformer network"
    - "unsupervised representation learning by predicting image rotations"
    - "spatio temporal channel correlation networks for action classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "temporal reasoning graph for activity recognition"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "linkage based face clustering via graph convolution network"
    - "multi label image recognition with graph convolutional networks"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "graph neural networks a review of methods and applications"
    - "learning discriminative video representations using adversarial perturbations"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

Connected through hyperedge HE-5 (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-6 (induced by source paper "learning video representations from correspondence proposals"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "flownet3d learning scene flow in 3d point clouds"
    - "motion feature network fixed motion filter for action recognition"
    - "deep sets"
    - "dynamic graph cnn for learning on point clouds"
    - "motion fused frames data level fusion strategy for hand gesture recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-7 (induced by source paper "lp 3dcnn unveiling local phase in 3d convolutional neural networks"):
  with members:
    - "modelling local phase of images and textures with applications in phase denoising and phase retrieval"
    - "spatio temporal channel correlation networks for action classification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-8 (induced by source paper "cooperative cross stream network for discriminative action representation"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "on the effectiveness of task granularity for transfer learning"
    - "video representation learning using discriminative pooling"
    - "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"
    - "in defense of the triplet loss for person re identification"

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)