human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "recognition of advertisement emotions with application to computational advertising"):
  Abstract: Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience. However, most computational affect recognition (AR) approaches examine ads via the text modality, and only limited work has been devoted to decoding ad emotions from audiovisual or user cues. This work (1) compiles an affective ad dataset capable of evoking coherent emotions across users; (2) explores the efficacy of content-centric convolutional neural network (CNN) features for AR vis-a-vis handcrafted audio-visual descriptors; (3) examines user-centric ad AR from Electroencephalogram (EEG) responses acquired during ad-viewing, and (4) demonstrates how better affect predictions facilitate effective computational advertising as determined by a study involving 18 users. Experiments reveal that (a) CNN features outperform audiovisual descriptors for content-centric AR; (b) EEG features are able to encode ad-induced emotions better than content-based features; (c) Multi-task learning performs best among a slew of classification algorithms to achieve optimal AR, and (d) Pursuant to (b), EEG features also enable optimized ad insertion onto streamed video, as compared to content-based or manual insertion techniques in terms of ad memorability and overall user experience.

Includes members:
  Member "dialoguernn an attentive rnn for emotion detection in conversations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task learning network for emotion recognition in conversation")
  Member "affect recognition in ads with application to computational advertising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning curiosity algorithms")
  Member "deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "eeg based evaluation of cognitive workload induced by acoustic parameters for data sonification")
  Member "evaluating content centric vs user centric ad affect recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey")
  Member "discovering gender differences in facial emotion recognition via implicit behavioral cues":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gender and emotion recognition with implicit user signals")
  Member "object detection can be improved using human derived contextual expectations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements")
  Member "deep feature learning for eeg recordings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepbrain towards personalized eeg interaction through attentional and embedded lstm learning")

Question: Please tell me which class the source paper 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)