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: "latent embeddings for collective activity recognition"
  Abstract: Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments.

Connected through hyperedge HE-1 (induced by source paper "learning to learn relation for important people detection in still images"):
  with members:
    - "relational deep reinforcement learning"
    - "personrank detecting important people in images"
    - "attention is all you need"
    - "relation networks for object detection"
    - "learning to compare relation network for few shot learning"
    - "massive exploration of neural machine translation architectures"
    - "ssd single shot multibox detector"
    - "vip finding important people in images"

Connected through hyperedge HE-2 (induced by source paper "non local netvlad encoding for video classification"):
  with members:
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "fine grained video attractiveness prediction using multimodal deep learning on a large real world dataset"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "video re localization"
    - "reconstruction network for video captioning"
    - "averaging weights leads to wider optima and better generalization"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-3 (induced by source paper "online collective animal movement activity recognition"):
  with members:
    - "adam a method for stochastic optimization"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "sequence to sequence learning with neural networks"
    - "generating sequences with recurrent neural networks"

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)