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: "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
  Abstract: We propose a multi-instance-learning (MIL) approach for weakly supervised learning problems, where a training set is formed by bags (sets of feature vectors or instances) and only labels at bag-level are provided. Specifically, we consider the multi-instance dynamic-ordinal-regression (MI-DOR) setting, where the instance labels are naturally represented as ordinal variables and bags are structured as temporal sequences. To this end, we propose MI dynamic ordinal random fields (MI-DORF). In this paper, we treat instance-labels as temporally dependent latent variables in an undirected graphical model. Different MIL assumptions are modelled via newly introduced high-order potentials relating bag and instance-labels within the energy function of the model. We also extend our framework to address the partially observed MI-DOR problem, where a subset of instance labels is also available during training. We show on the tasks of weakly supervised facial action unit and pain intensity estimation, that the proposed framework outperforms alternative learning approaches. Furthermore, we show that MI-DORF can be employed to reduce the data annotation efforts in this context by large-scale.

Connected through hyperedge HE-1 (induced by source paper "soft ranking label encoding for robust facial age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "c3ae exploring the limits of compact model for age estimation"
    - "deep representation learning with part loss for person re identification"
    - "fine grained age estimation in the wild with attention lstm networks"
    - "a coupled evolutionary network for age estimation"
    - "deep label distribution learning with label ambiguity"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "automatic detection of protective behavior in chronic pain physical rehabilitation a recurrent neural network approach"):
  with members:
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "regularizing face verification nets for pain intensity regression"
    - "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"

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)