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: "affect recognition in ads with application to computational advertising"
  Abstract: Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors[9] upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.

Connected through hyperedge HE-1 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"

Connected through hyperedge HE-2 (induced by source paper "recognition of advertisement emotions with application to computational advertising"):
  with members:
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "learning transferable architectures for scalable image recognition"
    - "deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders"
    - "evaluating content centric vs user centric ad affect recognition"
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "object detection can be improved using human derived contextual expectations"
    - "deep feature learning for eeg recordings"
    - "dexpression deep convolutional neural network for expression recognition"

Connected through hyperedge HE-3 (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"
    - "an eeg based image annotation system"
    - "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-4 (induced by source paper "engagement estimation in advertisement videos with eeg"):
  with members:
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "evaluating content centric vs user centric ad affect recognition"
    - "a data driven study of view duration on youtube"

Connected through hyperedge HE-5 (induced by source paper "an eeg based image annotation system"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "gender and emotion recognition with implicit user signals"
    - "deep learning human mind for automated visual classification"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "gender and emotion recognition with implicit user signals"):
  with members:
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "multimodal emotion recognition using multimodal deep learning"
    - "human gender classification a review"
    - "vision based human gender recognition a survey"

Connected through hyperedge HE-7 (induced by source paper "evaluating content centric vs user centric ad affect recognition"):
  with members:
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "object detection can be improved using human derived contextual expectations"
    - "caffe convolutional architecture for fast feature embedding"

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.HC(Human-Computer Interaction)