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: "discovering gender differences in facial emotion recognition via implicit behavioral cues"
  Abstract: We examine the utility of implicit behavioral cues in the form of EEG brain signals and eye movements for gender recognition (GR) and emotion recognition (ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded (no mask) as well as partially occluded (eye and mouth masked) emotive faces. Obtained experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing especially for negative emotions is observed for males and females and (c) some of these cognitive differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.

Connected through hyperedge HE-1 (induced by source paper "recognition of advertisement emotions with application to computational advertising"):
  with members:
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "affect recognition in ads with application to computational advertising"
    - "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"
    - "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-2 (induced by source paper "gender and emotion recognition with implicit user signals"):
  with members:
    - "affect recognition in ads with application to computational advertising"
    - "multimodal emotion recognition using multimodal deep learning"
    - "human gender classification a review"
    - "vision based human gender recognition a survey"

Connected through hyperedge HE-3 (induced by source paper "evaluating content centric vs user centric ad affect recognition"):
  with members:
    - "affect recognition in ads with application to computational advertising"
    - "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)