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: "multimedia stimuli databases usage patterns a survey report"
  Abstract: Multimedia documents such as images, sounds or videos can be used to elicit emotional responses in exposed human subjects. These stimuli are stored in affective multimedia databases and successfully used for a wide variety of research in affective computing, human-computer interaction and cognitive sciences. Affective multimedia databases are simple repositories of multimedia documents with annotated high-level semantics and affective content. Although important all affective multimedia databases have numerous deficiencies which impair their applicability. To establish a better understanding of how experts use affective multimedia databases an online survey was conducted into the subject. The survey results are statistically significant and indicate that contemporary databases lack stimuli with rich semantic and emotional content. 73.33% of survey participants find the databases lacking at least some important semantic or emotion content. Most of the participants consider stimuli descriptions to be inadequate. Overall, 1-2h or more than 24h are generally needed to construct a single stimulation sequence. Almost 84% of the survey participants would like to use real-life videos in their research. Experts unequivocally recognize the need for an intelligent stimuli retrieval application that would assist them in experimentation. Almost all experts agree such applications could be useful in their work.

Connected through hyperedge HE-1 (induced by source paper "eev dataset predicting expressions evoked by diverse videos"):
  with members:
    - "gla in mediaeval 2018 emotional impact of movies task"
    - "a compact embedding for facial expression similarity"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learnable pooling with context gating for video classification"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "gwat the geneva affective picture database wordnet annotation tool"):
  with members:
    - "stimont a core ontology for multimedia stimuli description"
    - "wntags a web based tool for image labeling and retrieval with lexical ontologies"
    - "tagging multimedia stimuli with ontologies"

Connected through hyperedge HE-3 (induced by source paper "retrieval of multimedia stimuli with semantic and emotional cues suggestions from a controlled study"):
  with members:
    - "stimont a core ontology for multimedia stimuli description"
    - "tagging multimedia stimuli with ontologies"

Connected through hyperedge HE-4 (induced by source paper "stimont a core ontology for multimedia stimuli description"):
  with members:
    - "wntags a web based tool for image labeling and retrieval with lexical ontologies"
    - "tagging multimedia stimuli with ontologies"

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.MM(Multimedia)