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: "multimodal affect recognition using kinect"
  Abstract: Affect (emotion) recognition has gained significant attention from researchers in the past decade. Emotion-aware computer systems and devices have many applications ranging from interactive robots, intelligent online tutor to emotion based navigation assistant. In this research data from multiple modalities such as face, head, hand, body and speech was utilized for affect recognition. The research used color and depth sensing device such as Kinect for facial feature extraction and tracking human body joints. Temporal features across multiple frames were used for affect recognition. Event driven decision level fusion was used to combine the results from each individual modality using majority voting to recognize the emotions. The study also implemented affect recognition by matching the features to the rule based emotion templates per modality. Experiments showed that multimodal affect recognition rates using combination of emotion templates and supervised learning were better compared to recognition rates based on supervised learning alone. Recognition rates obtained using temporal feature were higher compared to recognition rates obtained using position based features only.

Connected through hyperedge HE-1 (induced by source paper "human action recognition and assessment via deep neural network self organization"):
  with members:
    - "rethinking continual learning for autonomous agents and robots"
    - "continual lifelong learning with neural networks a review"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "an incremental self organizing architecture for sensorimotor learning and prediction"
    - "a self organizing neural network architecture for learning human object interactions"
    - "continual learning through synaptic intelligence"
    - "icarl incremental classifier and representation learning"

Connected through hyperedge HE-2 (induced by source paper "multimodal affect analysis for product feedback assessment"):
  with members:
    - "xml entity architecture for efficient software integration"
    - "embracing agile methodology during devops developer internship program"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-3 (induced by source paper "edge based grid super imposition for crowd emotion recognition"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "code definition analysis for call graph generation"
    - "structured unit testable templated code for efficient code review process"
    - "dynamic probabilistic network based human action recognition"
    - "self contained cross cutting pipeline software architecture"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "xml entity architecture for efficient software integration"
    - "emofit affect monitoring system for sedentary jobs"

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)