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: "amalgamating knowledge towards comprehensive classification"
  Abstract: With the rapid development of deep learning, there have been an unprecedentedly large number of trained deep network models available online. Reusing such trained models can significantly reduce the cost of training the new models from scratch, if not infeasible at all as the annotations used for the training original networks are often unavailable to public. We propose in this paper to study a new model-reusing task, which we term as \emph{knowledge amalgamation}. Given multiple trained teacher networks, each of which specializes in a different classification problem, the goal of knowledge amalgamation is to learn a lightweight student model capable of handling the comprehensive classification. We assume no other annotations except the outputs from the teacher models are available, and thus focus on extracting and amalgamating knowledge from the multiple teachers. To this end, we propose a pilot two-step strategy to tackle the knowledge amalgamation task, by learning first the compact feature representations from teachers and then the network parameters in a layer-wise manner so as to build the student model. We apply this approach to four public datasets and obtain very encouraging results: even without any human annotation, the obtained student model is competent to handle the comprehensive classification task and in most cases outperforms the teachers in individual sub-tasks.

Connected through hyperedge HE-1 (induced by source paper "highlight every step knowledge distillation via collaborative teaching"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "improving fast segmentation with teacher student learning"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"):
  with members:
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi and weakly supervised semantic segmentation using generative adversarial network"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "co evolutionary compression for unpaired image translation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-4 (induced by source paper "customizing student networks from heterogeneous teachers via adaptive knowledge amalgamation"):
  with members:
    - "knowledge amalgamation from heterogeneous networks by common feature learning"
    - "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"
    - "knowledge flow improve upon your teachers"
    - "multi task zipping via layer wise neuron sharing"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "few sample knowledge distillation for efficient network compression"
    - "single shot refinement neural network for object detection"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-5 (induced by source paper "dafl data free learning of student networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "interpretable convolutional neural networks"
    - "data free knowledge distillation for deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "data free parameter pruning for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "feed feature level ensemble for knowledge distillation"):
  with members:
    - "paraphrasing complex network network compression via factor transfer"
    - "knowledge distillation by on the fly native ensemble"
    - "efficient neural architecture search via parameter sharing"
    - "deep mutual learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "data distillation towards omni supervised learning"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "distilling a neural network into a soft decision tree"

Connected through hyperedge HE-7 (induced by source paper "knowledge amalgamation from heterogeneous networks by common feature learning"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep networks with stochastic depth"
    - "swapout learning an ensemble of deep architectures"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "amalgamating filtered knowledge learning task customized student from multi task teachers"):
  with members:
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "deep label distribution learning with label ambiguity"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "hcp a flexible cnn framework for multi label image classification"
    - "cross stitch networks for multi task learning"
    - "identity mappings in deep residual networks"
    - "cnn rnn a unified framework for multi label image classification"

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)