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: "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
  Abstract: We propose an heterogeneous multi-task learning framework for human pose estimation from monocular image with deep convolutional neural network. In particular, we simultaneously learn a pose-joint regressor and a sliding-window body-part detector in a deep network architecture. We show that including the body-part detection task helps to regularize the network, directing it to converge to a good solution. We report competitive and state-of-art results on several data sets. We also empirically show that the learned neurons in the middle layer of our network are tuned to localized body parts.

Connected through hyperedge HE-1 (induced by source paper "deep multi task augmented feature learning via hierarchical graph neural network"):
  with members:
    - "graph star net for generalized multi task learning"
    - "multi task learning over graph structures"
    - "learning to multitask"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-2 (induced by source paper "interpretable mtl from heterogeneous domains using boosted tree"):
  with members:
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "visual tracking via dynamic memory networks"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "learning dynamic memory networks for object tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "mavot memory augmented video object tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "dual deep network for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "cross domain complementary learning with synthetic data for multi person part segmentation"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning to adapt structured output space for semantic segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "densepose dense human pose estimation in the wild"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "many task learning with task routing"):
  with members:
    - "dropblock a regularization method for convolutional networks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "shakedrop regularization for deep residual learning"

Connected through hyperedge HE-6 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "direct modelling of speech emotion from raw speech"
    - "automated screening for distress a perspective for the future"
    - "attention augmented end to end multi task learning for emotion prediction from speech"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "flipped adversarial autoencoders"
    - "transfer learning for improving speech emotion classification accuracy"
    - "leveraging unlabeled data for crowd counting by learning to rank"

Connected through hyperedge HE-7 (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"
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "deep label distribution learning with label ambiguity"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

Connected through hyperedge HE-8 (induced by source paper "distill 2md mtl data distillation based on multi dataset multi domain multi task frame work to solve face related tasksks multi task learning semi supervised learning"):
  with members:
    - "model compression via distillation and quantization"
    - "deep facial expression recognition a survey"
    - "data distillation towards omni supervised learning"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "cyclical learning rates for training neural networks"

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)