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: "augmenting supervised emotion recognition with rule based decision model"
  Abstract: The aim of this research is development of rule based decision model for emotion recognition. This research also proposes using the rules for augmenting inter-corporal recognition accuracy in multimodal systems that use supervised learning techniques. The classifiers for such learning based recognition systems are susceptible to over fitting and only perform well on intra-corporal data. To overcome the limitation this research proposes using rule based model as an additional modality. The rules were developed using raw feature data from visual channel, based on human annotator agreement and existing studies that have attributed movement and postures to emotions. The outcome of the rule evaluations was combined during the decision phase of emotion recognition system. The results indicate rule based emotion recognition augment recognition accuracy of learning based systems and also provide better recognition rate across inter corpus emotion test data.

Connected through hyperedge HE-1 (induced by source paper "survey on emotional body gesture recognition"):
  with members:
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "3d human pose estimation 2d pose estimation matching"
    - "monocular 3d human pose estimation in the wild using improved cnn supervision"
    - "associative embedding end to end learning for joint detection and grouping"
    - "human pose estimation with iterative error feedback"

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"
    - "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"
    - "multimodal affect analysis for product feedback assessment"
    - "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 "automated prediction of temporal relations"):
  with members:
    - "aggressive actions and anger detection from multiple modalities using kinect"
    - "xml entity architecture for efficient software integration"
    - "embracing agile methodology during devops developer internship program"
    - "affect intensity estimation using multiple modalities"
    - "learning semantic script knowledge with event embeddings"
    - "learning sentence internal temporal relations"

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)