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: "learning multiple tasks with multilinear relationship networks"
  Abstract: Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and improve feature transferability in the multiple task-specific layers. This paper presents Multilinear Relationship Networks (MRN) that discover the task relationships based on novel tensor normal priors over parameter tensors of multiple task-specific layers in deep convolutional networks. By jointly learning transferable features and multilinear relationships of tasks and features, MRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Experiments show that MRN yields state-of-the-art results on three multi-task learning datasets.

Connected through hyperedge HE-1 (induced by source paper "dynamic task weighting methods for multi task networks in autonomous driving systems"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "which tasks should be learned together in multi task learning"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-2 (induced by source paper "task assisted domain adaptation with anchor tasks"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "taskonomy disentangling task transfer learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "regularizing deep networks by modeling and predicting label structure"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-3 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "branched multi task networks deciding what layers to share"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "routing networks and the challenges of modular and compositional computation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "attentive single tasking of multiple tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-4 (induced by source paper "cross dataset training for class increasing object detection"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "integrated face analytics networks through cross dataset hybrid training"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-5 (induced by source paper "unsupervised and interpretable domain adaptation to rapidly filter social web data for emergency services"):
  with members:
    - "diversity based generalization for neural unsupervised text classification under domain shift"
    - "domain adaptation with adversarial training and graph embeddings"
    - "advances in pre training distributed word representations"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "effective approaches to attention based neural machine translation"
    - "learning multiple tasks with deep relationship networks"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a simple general approach to balance task difficulty in multi task learning"):
  with members:
    - "pareto multi task learning"
    - "multi task learning as multi objective optimization"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial multi task learning for text classification"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "deep multi task learning via generalized tensor trace norm"):
  with members:
    - "learning to multitask"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "very deep convolutional networks for large scale image recognition"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-8 (induced by source paper "auxiliary learning for deep multi task learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "ocnet object context network for scene parsing"
    - "deep ordinal regression network for monocular depth estimation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "discrimination aware channel pruning for deep 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.LG(Machine Learning)