human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep weakly supervised domain adaptation for pain localization in videos"):
  Abstract: Automatic pain assessment has an important potential diagnostic value for people having difficulties in articulating their pain experiences. Facial Expression plays a crucial role as one of the dominating nonverbal channels for eliciting pain expression events, which has been widely used for estimating the pain intensity of individual videos. However, using state-of-the-art deep learning (DL) models in real-world pain estimation applications raises several challenges related to considerable subjective variations of facial expressions, operational capture conditions and lack of representative training videos with labels. Given the cost of annotating intensity levels for every video frame, a weakly-supervised domain adaptation (WSDA) technique is proposed for training DL models for spatio-temporal pain intensity estimation from videos. Deep domain adaptation to operational conditions relies on target domain videos that are weakly-labeled -- labels for pain intensity levels are provided on a periodic basis, for each video sequence. In particular, WSDA integrates multiple instance learning into an adversarial deep domain adaptation framework to train an Inflated 3D-CNN (I3D) model so that it can accurately estimate pain intensities in the target operational domain. The training mechanism leverages weak supervision loss, along with domain loss and source supervision loss for domain adaptation of I3D models. Experimental results obtained using labeled source domain videos from the RECOLA dataset and weakly-labeled target domain UNBC-McMaster videos indicate that the proposed deep WSDA approach can achieve a significantly higher level of sequence (bag)-level and frame (instance)-level pain localization accuracy than related state-of-the-art approaches.

Includes members:
  Member "deep spatiotemporal representation of the face for automatic pain intensity estimation":
    also connected through other hyperedges:
      (none)
  Member "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "soft ranking label encoding for robust facial age estimation")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "multiple instance learning a survey of problem characteristics and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement")
  Member "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepsat v2 feature augmented convolutional neural nets for satellite image classification")
  Member "learning pain from action unit combinations a weakly supervised approach via multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automated pain detection from facial expressions using facs a review")
  Member "regularizing face verification nets for pain intensity regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automatic detection of protective behavior in chronic pain physical rehabilitation a recurrent neural network approach")
  Member "recurrent convolutional neural network regression for continuous pain intensity estimation in video":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "emopain challenge 2020 multimodal pain evaluation from facial and bodily expressions")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)