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: "learning pain from action unit combinations a weakly supervised approach via multiple instance learning"
  Abstract: Facial pain expression is an important modality for assessing pain, especially when a patient's verbal ability to communicate is impaired. A set of eight facial muscle based action units (AUs), which are defined by the Facial Action Coding System (FACS), have been widely studied and are highly reliable for pain detection through facial expressions. However, using FACS is a very time consuming task that makes its clinical use prohibitive. An automated facial expression recognition system (AFER) reliably detecting pain-related AUs would be highly beneficial for efficient and practical pain monitoring. Automated pain detection under clinical settings is viewed as a weakly supervised problem, which is not suitable general AFER system that trained on well labeled data. Existing pain oriented AFER research either focus on the individual pain-related AU recognition or bypassing the AU detection procedure by training a binary pain classifier from pain intensity data. In this paper, we decouple pain detection into two consecutive tasks: the AFER based AU labeling at video frame level and a probabilistic measure of pain at sequence level from AU combination scores. Our work is distinguished in the following aspects, 1) State of the art AFER tools Emotient is applied on pain oriented data sets for single AU labeling. 2) Two different data structures are proposed to encode AU combinations from single AU scores, which forms low-dimensional feature vectors for the learning framework. 3) Two weakly supervised learning frameworks namely multiple instance learning and multiple clustered instance learning are employed corresponding to each feature structure to learn pain from video sequences. The results shows 87% pain recognition accuracy with 0.94 AUC on UNBC-McMaster dataset. Tests on Wilkie's dataset suggests the potential value of the proposed system for pain monitoring task under clinical settings.

Connected through hyperedge HE-1 (induced by source paper "deep weakly supervised domain adaptation for pain localization in videos"):
  with members:
    - "deep spatiotemporal representation of the face for automatic pain intensity estimation"
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation"
    - "regularizing face verification nets for pain intensity regression"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "automated pain detection from facial expressions using facs a review"):
  with members:
    - "deep structured learning for facial action unit intensity estimation"
    - "deepfacelift interpretable personalized models for automatic estimation of self reported pain"
    - "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation"
    - "lomo latent ordinal model for facial analysis in videos"
    - "machine based multimodal pain assessment tool for infants a review"
    - "very deep convolutional networks for large scale image recognition"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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.LG(Machine Learning)