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: "beyond gaussian pyramid multi skip feature stacking for action recognition"
  Abstract: Most state-of-the-art action feature extractors involve differential operators, which act as highpass filters and tend to attenuate low frequency action information. This attenuation introduces bias to the resulting features and generates ill-conditioned feature matrices. The Gaussian Pyramid has been used as a feature enhancing technique that encodes scale-invariant characteristics into the feature space in an attempt to deal with this attenuation. However, at the core of the Gaussian Pyramid is a convolutional smoothing operation, which makes it incapable of generating new features at coarse scales. In order to address this problem, we propose a novel feature enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks features extracted using a family of differential filters parameterized with multiple time skips and encodes shift-invariance into the frequency space. MIFS compensates for information lost from using differential operators by recapturing information at coarse scales. This recaptured information allows us to match actions at different speeds and ranges of motion. We prove that MIFS enhances the learnability of differential-based features exponentially. The resulting feature matrices from MIFS have much smaller conditional numbers and variances than those from conventional methods. Experimental results show significantly improved performance on challenging action recognition and event detection tasks. Specifically, our method exceeds the state-of-the-arts on Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup strategy for feature extraction with minimal or no accuracy cost.

Connected through hyperedge HE-1 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "skeleton aided articulated motion generation"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-2 (induced by source paper "tensor analysis with n mode generalized difference subspace"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-3 (induced by source paper "videos as space time region graphs"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "segmentation aware convolutional networks using local attention masks"
    - "joint discovery of object states and manipulation actions"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal fusion networks for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "rgb d based human motion recognition with deep learning a survey"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "optical flow guided feature a fast and robust motion representation for video action recognition"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "temporal convolutional networks for action segmentation and detection"

Connected through hyperedge HE-5 (induced by source paper "deep discriminative model for video classification"):
  with members:
    - "rank pooling for action recognition"
    - "long term temporal convolutions for action recognition"
    - "dynamic texture and scene classification by transferring deep image features"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "beyond short snippets deep networks for video classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-6 (induced by source paper "exploiting feature and class relationships in video categorization with regularized deep neural networks"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "beyond short snippets deep networks for video classification"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "going deeper with convolutions"
    - "two stream convolutional networks for action recognition in videos"
    - "using web co occurrence statistics for improving image categorization"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "end to end video level representation learning for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep local video feature for action recognition"
    - "learning gating convnet for two stream based methods in action recognition"
    - "temporal segment networks for action recognition in videos"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "eigen evolution pooling for human action recognition"

Connected through hyperedge HE-8 (induced by source paper "discriminatively learned hierarchical rank pooling networks"):
  with members:
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "action recognition with dynamic image networks"
    - "youtube 8m a large scale video classification benchmark"
    - "convolutional two stream network fusion for video action recognition"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "exploiting image trained cnn architectures for unconstrained video classification"

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)