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: "principled parallel mean field inference for discrete random fields"
  Abstract: Mean-field variational inference is one of the most popular approaches to inference in discrete random fields. Standard mean-field optimization is based on coordinate descent and in many situations can be impractical. Thus, in practice, various parallel techniques are used, which either rely on ad-hoc smoothing with heuristically set parameters, or put strong constraints on the type of models. In this paper, we propose a novel proximal gradient-based approach to optimizing the variational objective. It is naturally parallelizable and easy to implement. We prove its convergence, and then demonstrate that, in practice, it yields faster convergence and often finds better optima than more traditional mean-field optimization techniques. Moreover, our method is less sensitive to the choice of parameters.

Connected through hyperedge HE-1 (induced by source paper "on regularized losses for weakly supervised cnn segmentation"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "efficient linear programming for dense crfs"
    - "efficient continuous relaxations for dense crf"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "deep occlusion reasoning for multi camera multi target detection"):
  with members:
    - "the wildtrack multi camera person dataset"
    - "deep multi camera people detection"
    - "how far are we from solving pedestrian detection"
    - "ssd single shot multibox detector"
    - "multi modal mean fields via cardinality based clamping"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "higher order potentials in end to end trainable conditional random fields"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "dope distributed optimization for pairwise energies"):
  with members:
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "secrets of grabcut and kernel k means"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-4 (induced by source paper "social scene understanding end to end multi person action localization and collective activity recognition"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "you only look once unified real time object detection"
    - "filtered channel features for pedestrian detection"
    - "spatial transformer networks"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "a hierarchical deep temporal model for group activity recognition"

Connected through hyperedge HE-5 (induced by source paper "multi modal mean fields via cardinality based clamping"):
  with members:
    - "joint optical flow and temporally consistent semantic segmentation"
    - "higher order potentials in end to end trainable conditional random fields"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "clamping improves trw and mean field approximations"
    - "deepmatching hierarchical deformable dense matching"
    - "mean field networks"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

Connected through hyperedge HE-6 (induced by source paper "efficient continuous relaxations for dense crf"):
  with members:
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "block coordinate frank wolfe optimization for structural svms"
    - "map estimation of semi metric mrfs via hierarchical graph cuts"

Connected through hyperedge HE-7 (induced by source paper "higher order conditional random fields in deep neural networks"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "deep structured models for group activity recognition"
    - "deeply learning the messages in message passing inference"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"
    - "attention to scale scale aware semantic image segmentation"
    - "multi scale context aggregation by dilated convolutions"

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)