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: "visual recognition by counting instances a multi instance cardinality potential kernel"
  Abstract: Many visual recognition problems can be approached by counting instances. To determine whether an event is present in a long internet video, one could count how many frames seem to contain the activity. Classifying the activity of a group of people can be done by counting the actions of individual people. Encoding these cardinality relationships can reduce sensitivity to clutter, in the form of irrelevant frames or individuals not involved in a group activity. Learned parameters can encode how many instances tend to occur in a class of interest. To this end, this paper develops a powerful and flexible framework to infer any cardinality relation between latent labels in a multi-instance model. Hard or soft cardinality relations can be encoded to tackle diverse levels of ambiguity. Experiments on tasks such as human activity recognition, video event detection, and video summarization demonstrate the effectiveness of using cardinality relations for improving recognition results.

Connected through hyperedge HE-1 (induced by source paper "learning actor relation graphs for group activity recognition"):
  with members:
    - "linknet relational embedding for scene graph"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "graph r cnn for scene graph generation"
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "progressive relation learning for group activity recognition"):
  with members:
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "learning actor relation graphs for group activity recognition"
    - "convolutional relational machine for group activity recognition"
    - "a comprehensive survey on graph neural networks"
    - "graph networks as learnable physics engines for inference and control"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "structural recurrent neural network srnn for group activity analysis"
    - "skeleton based action recognition with synchronous local and non local spatio temporal learning and frequency attention"

Connected through hyperedge HE-3 (induced by source paper "convolutional relational machine for group activity recognition"):
  with members:
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised learning of long term motion dynamics for videos"
    - "cern confidence energy recurrent network for group activity recognition"
    - "convolutional pose machines"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-4 (induced by source paper "multi level sequence gan for group activity recognition"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "structural recurrent neural network srnn for group activity analysis"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "cern confidence energy recurrent network for group activity recognition"

Connected through hyperedge HE-5 (induced by source paper "a multi stream convolutional neural network framework for group activity recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "convolutional pose machines"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "hierarchical long short term concurrent memory for human interaction recognition"):
  with members:
    - "structural recurrent neural network srnn for group activity analysis"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "spatial structural and temporal feature learning for human interaction prediction"
    - "deep structured models for group activity recognition"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "fast r cnn"
    - "differential recurrent neural networks for action recognition"

Connected through hyperedge HE-7 (induced by source paper "predict and constrain modeling cardinality in deep structured prediction"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "unrolled generative adversarial networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "highway and residual networks learn unrolled iterative estimation"
    - "structured prediction energy networks"
    - "fully connected deep structured networks"
    - "learning deep structured models"
    - "fast exact inference for recursive cardinality models"

Connected through hyperedge HE-8 (induced by source paper "who did what at where and when simultaneous multi person tracking and activity recognition"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "poi multiple object tracking with high performance detection and appearance feature"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "deep structured models for group activity recognition"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity 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)