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: "bottom up and top down reasoning with convolutional latent variable models"
  Abstract: Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a "unidirectional" bottom-up feed-forward fashion. However, biological evidence suggests that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work introduces "bidirectional" architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by treating units as latent variables in a global energy function. We call our models convolutional latent-variable models (CLVMs). From a theoretical perspective, CLVMs unify several approaches for recognition, including CNNs, generative deep models (e.g., Boltzmann machines), and discriminative latent-variable models (e.g., DPMs). #R##N#From a practical perspective, CLVMs are particularly well-suited for multi-task learning. We describe a single architecture that simultaneously achieves state-of-the-art accuracy for tasks spanning both high-level recognition (part detection/localization) and low-level grouping (pixel segmentation). Bidirectional reasoning is particularly helpful for detailed low-level tasks, since they can take advantage of top-down feedback. Our architectures are quite efficient, capable of processing an image in milliseconds. We present results on benchmark datasets with both part/keypoint labels and segmentation masks (such as PASCAL and LFW) that demonstrate a significant improvement over prior art, in both speed and accuracy.

Connected through hyperedge HE-1 (induced by source paper "human pose regression by combining indirect part detection and contextual information"):
  with members:
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "compositional human pose regression"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "face alignment by local deep descriptor regression"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "finding tiny faces"
    - "locnet improving localization accuracy for object detection"

Connected through hyperedge HE-3 (induced by source paper "3d human pose estimation 2d pose estimation matching"):
  with members:
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "bottom up and top down reasoning with hierarchical rectified gaussians"
    - "a dual source approach for 3d pose estimation from a single image"
    - "direct prediction of 3d body poses from motion compensated sequences"
    - "robust estimation of 3d human poses from a single image"

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)