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: "variable state latent conditional random fields for facial expression recognition and action unit detection"
  Abstract: Automated recognition of facial expressions of emotions, and detection of facial action units (AUs), from videos depends critically on modeling of their dynamics. These dynamics are characterized by changes in temporal phases (onset-apex-offset) and intensity of emotion expressions and AUs, the appearance of which may vary considerably among target subjects, making the recognition/detection task very challenging. The state-of-the-art Latent Conditional Random Fields (L-CRF) framework allows one to efficiently encode these dynamics through the latent states accounting for the temporal consistency in emotion expression and ordinal relationships between its intensity levels, these latent states are typically assumed to be either unordered (nominal) or fully ordered (ordinal). Yet, such an approach is often too restrictive. For instance, in the case of AU detection, the goal is to discriminate between the segments of an image sequence in which this AU is active or inactive. While the sequence segments containing activation of the target AU may better be described using ordinal latent states, the inactive segments better be described using unordered (nominal) latent states, as no assumption can be made about their underlying structure (since they can contain either neutral faces or activations of non-target AUs). To address this, we propose the variable-state L-CRF (VSL-CRF) model that automatically selects the optimal latent states for the target image sequence. To reduce the model overfitting either the nominal or ordinal latent states, we propose a novel graph-Laplacian regularization of the latent states. Our experiments on three public expression databases show that the proposed model achieves better generalization performance compared to traditional L-CRFs and other related state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "sewa db a rich database for audio visual emotion and sentiment research in the wild"):
  with members:
    - "avec 2016 depression mood and emotion recognition workshop and challenge"

Connected through hyperedge HE-2 (induced by source paper "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "multi instance dynamic ordinal random fields for weakly supervised pain intensity estimation"
    - "multiple instance learning by discriminative training of markov networks"
    - "fast exact inference for recursive cardinality models"

Connected through hyperedge HE-3 (induced by source paper "multi instance dynamic ordinal random fields for weakly supervised pain intensity estimation"):
  with members:
    - "multiple instance learning by discriminative training of markov networks"
    - "fast exact inference for recursive cardinality models"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-4 (induced by source paper "machine based multimodal pain assessment tool for infants a review"):
  with members:
    - "deepfacelift interpretable personalized models for automatic estimation of self reported pain"
    - "estimation of infants cry fundamental frequency using a modified sift algorithm"

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)