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: "fine grained activity recognition in baseball videos"
  Abstract: In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch speed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.

Connected through hyperedge HE-1 (induced by source paper "differentiable grammars for videos"):
  with members:
    - "temporal gaussian mixture layer for videos"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "recurrent neural network grammars"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-2 (induced by source paper "a context aware loss function for action spotting in soccer videos"):
  with members:
    - "golfdb a video database for golf swing sequencing"
    - "gaussian temporal awareness networks for action localization"
    - "pan tilt zoom slam for sports videos"
    - "bmn boundary matching network for temporal action proposal generation"
    - "diagnosing error in temporal action detectors"
    - "temporal hockey action recognition via pose and optical flows"
    - "taskonomy disentangling task transfer learning"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"

Connected through hyperedge HE-3 (induced by source paper "zero shot action recognition in videos a survey"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "human action recognition and prediction a survey"
    - "towards universal representation for unseen action recognition"
    - "a short note about kinetics 600"
    - "graph convolutional networks for text classification"
    - "visual data synthesis via gan for zero shot video classification"
    - "cross modal and hierarchical modeling of video and text"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "learning multimodal representations for unseen activities"):
  with members:
    - "joint event detection and description in continuous video streams"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "zero shot learning via class conditioned deep generative models"
    - "dense captioning events in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from video and text via large scale discriminative clustering"

Connected through hyperedge HE-5 (induced by source paper "tiny video networks"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "searching for mobilenetv3"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "moments in time dataset one million videos for event understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "motion feature network fixed motion filter for action recognition"

Connected through hyperedge HE-6 (induced by source paper "learning differentiable grammars for continuous data"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "predictive corrective networks for action detection"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "recurrent neural network grammars"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "asynchronous temporal fields for action recognition"
    - "every moment counts dense detailed labeling of actions in complex videos"

Connected through hyperedge HE-7 (induced by source paper "uav gesture a dataset for uav control and gesture recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "attentional pooling for action recognition"
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "convolutional pose machines"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "review of action recognition and detection methods"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-8 (induced by source paper "early detection of injuries in mlb pitchers from video"):
  with members:
    - "evolving space time neural architectures for videos"
    - "representation flow for action recognition"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "predictive corrective networks for action detection"
    - "on the integration of optical flow and action recognition"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "the kinetics human action video dataset"

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)