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: "affect intensity estimation using multiple modalities"
  Abstract: One of the challenges in affect recognition is accurate estimation of the emotion intensity level. This research proposes development of an affect intensity estimation model based on a weighted sum of classification confidence levels, displacement of feature points and speed of feature point motion. The parameters of the model were calculated from data captured using multiple modalities such as face, body posture, hand movement and speech. A preliminary study was conducted to compare the accuracy of the model with the annotated intensity levels. An emotion intensity scale ranging from 0 to 1 along the arousal dimension in the emotion space was used. Results indicated speech and hand modality significantly contributed in improving accuracy in emotion intensity estimation using the proposed model.

Connected through hyperedge HE-1 (induced by source paper "multimodal affect analysis for product feedback assessment"):
  with members:
    - "multimodal affect recognition using kinect"
    - "xml entity architecture for efficient software integration"
    - "embracing agile methodology during devops developer internship program"

Connected through hyperedge HE-2 (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"
    - "multimodal affect recognition using kinect"
    - "xml entity architecture for efficient software integration"

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"
    - "multimodal affect analysis for product feedback assessment"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "xml entity architecture for efficient software integration"
    - "learning the structure of dynamic probabilistic networks"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-4 (induced by source paper "automated prediction of temporal relations"):
  with members:
    - "aggressive actions and anger detection from multiple modalities using kinect"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "xml entity architecture for efficient software integration"
    - "embracing agile methodology during devops developer internship program"
    - "learning semantic script knowledge with event embeddings"
    - "learning sentence internal temporal relations"

Connected through hyperedge HE-5 (induced by source paper "embracing agile methodology during devops developer internship program"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "structured unit testable templated code for efficient code review process"
    - "xml entity architecture for efficient software integration"
    - "emofit affect monitoring system for sedentary jobs"

Connected through hyperedge HE-6 (induced by source paper "aggressive actions and anger detection from multiple modalities using kinect"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "structured unit testable templated code for efficient code review process"
    - "xml entity architecture for efficient software integration"

Connected through hyperedge HE-7 (induced by source paper "self contained cross cutting pipeline software architecture"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "xml entity architecture for efficient software integration"

Connected through hyperedge HE-8 (induced by source paper "multimodal affect recognition using kinect"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "xml entity architecture for efficient software integration"

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)