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: "an eeg based image annotation system"
  Abstract: The success of deep learning in computer vision has greatly increased the need for annotated image datasets. We propose an EEG (Electroencephalogram)-based image annotation system. While humans can recognize objects in 20-200 milliseconds, the need to manually label images results in a low annotation throughput. Our system employs brain signals captured via a consumer EEG device to achieve an annotation rate of up to 10 images per second. We exploit the P300 event-related potential (ERP) signature to identify target images during a rapid serial visual presentation (RSVP) task. We further perform unsupervised outlier removal to achieve an F1-score of 0.88 on the test set. The proposed system does not depend on category-specific EEG signatures enabling the annotation of any new image category without any model pre-training.

Connected through hyperedge HE-1 (induced by source paper "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "yolov3 an incremental improvement"
    - "affect recognition in ads with application to computational advertising"
    - "evaluating content centric vs user centric ad affect recognition"
    - "automatic understanding of image and video advertisements"
    - "object detection can be improved using human derived contextual expectations"
    - "pet an eye tracking dataset for animal centric pascal object classes"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-2 (induced by source paper "eeg based evaluation of cognitive workload induced by acoustic parameters for data sonification"):
  with members:
    - "deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders"
    - "gender and emotion recognition with implicit user signals"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "very deep convolutional networks for large scale image 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)