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: "regularizing face verification nets for pain intensity regression"
  Abstract: Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.

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"
    - "learning pain from action unit combinations a weakly supervised approach via multiple instance learning"
    - "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 "automatic detection of protective behavior in chronic pain physical rehabilitation a recurrent neural network approach"):
  with members:
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised unconstrained action unit detection via feature disentanglement"):
  with members:
    - "drit diverse image to image translation via disentangled representations"
    - "adversarial discriminative domain adaptation"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-4 (induced by source paper "emotionet challenge recognition of facial expressions of emotion in the wild"):
  with members:
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "a simple fast and highly accurate algorithm to recover 3d shape from 2d landmarks on a single image"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "imagenet large scale visual recognition challenge"

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.CV(Computer Vision and Pattern Recognition)