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 analysis for product feedback assessment"
  Abstract: Consumers often react expressively to products such as food samples, perfume, jewelry, sunglasses, and clothing accessories. This research discusses a multimodal affect recognition system developed to classify whether a consumer likes or dislikes a product tested at a counter or kiosk, by analyzing the consumer's facial expression, body posture, hand gestures, and voice after testing the product. A depth-capable camera and microphone system - Kinect for Windows - is utilized. An emotion identification engine has been developed to analyze the images and voice to determine affective state of the customer. The image is segmented using skin color and adaptive threshold. Face, body and hands are detected using the Haar cascade classifier. Canny edges are identified and the lip, body and hand contours are extracted using spatial filtering. Edge count and orientation around the mouth, cheeks, eyes, shoulders, fingers and the location of the edges are used as features. Classification is done by an emotion template mapping algorithm and training a classifier using support vector machines. The real-time performance, accuracy and feasibility for multimodal affect recognition in feedback assessment are evaluated.

Connected through hyperedge HE-1 (induced by source paper "embedded implementation of a deep learning smile detector"):
  with members:
    - "elastic neural networks a scalable framework for embedded computer vision"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "end to end face detection and recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "face detection with the faster r cnn"
    - "xception deep learning with depthwise separable convolutions"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"

Connected through hyperedge HE-2 (induced by source paper "edge based grid super imposition for crowd emotion recognition"):
  with members:
    - "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"
    - "multimodal affect recognition using kinect"
    - "xml entity architecture for efficient software integration"
    - "emofit affect monitoring system for sedentary jobs"

Connected through hyperedge HE-3 (induced by source paper "dynamic probabilistic network based human action recognition"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "xml entity architecture for efficient software integration"
    - "affect intensity estimation using multiple modalities"
    - "learning the structure of dynamic probabilistic networks"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-4 (induced by source paper "embracing agile methodology during devops developer internship program"):
  with members:
    - "structured unit testable templated code for efficient code review process"
    - "xml entity architecture for efficient software integration"
    - "emofit affect monitoring system for sedentary jobs"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-5 (induced by source paper "aggressive actions and anger detection from multiple modalities using kinect"):
  with members:
    - "structured unit testable templated code for efficient code review process"
    - "xml entity architecture for efficient software integration"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-6 (induced by source paper "self contained cross cutting pipeline software architecture"):
  with members:
    - "xml entity architecture for efficient software integration"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-7 (induced by source paper "multimodal affect recognition using kinect"):
  with members:
    - "xml entity architecture for efficient software integration"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-8 (induced by source paper "xml entity architecture for efficient software integration"):
  with members:
    - "self contained cross cutting pipeline software architecture"
    - "affect intensity estimation using multiple modalities"

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)