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: "online deep learning learning deep neural networks on the fly"
  Abstract: Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings. In this paper, we present a new online deep learning framework that attempts to tackle the challenges by learning DNN models of adaptive depth from a sequence of training data in an online learning setting. In particular, we propose a novel Hedge Backpropagation (HBP) method for online updating the parameters of DNN effectively, and validate the efficacy of our method on large-scale data sets, including both stationary and concept drifting scenarios.

Connected through hyperedge HE-1 (induced by source paper "pa cache learning based popularity aware content caching in edge networks"):
  with members:
    - "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"
    - "what when and where to cache a unified optimization approach"
    - "optimizing timer based policies for general cache networks"
    - "understanding performance of edge content caching for mobile video streaming"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "fractalnet ultra deep neural networks without residuals"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "qactor on line active learning for noisy labeled stream data"):
  with members:
    - "dimensionality driven learning with noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "deep bayesian active learning with image data"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "intriguing properties of neural networks"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-3 (induced by source paper "physics guided deep neural networks for powerflow analysis"):
  with members:
    - "real time power system state estimation and forecasting via deep unrolled neural networks"
    - "data driven power flow linearization a regression approach"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "mapping rule estimation for power flow analysis in distribution grids"
    - "on the inverse power flow problem"

Connected through hyperedge HE-4 (induced by source paper "streaming active deep forest for evolving data stream classification"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "characterizing concept drift"

Connected through hyperedge HE-5 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-6 (induced by source paper "e2 train training state of the art cnns with over 80 energy savings"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "adascale towards real time video object detection using adaptive scaling"
    - "swalp stochastic weight averaging in low precision training"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-7 (induced by source paper "meth a family of high resolution and variable shape image challenges"):
  with members:
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "a study of bfloat16 for deep learning training"
    - "continual lifelong learning with neural networks a review"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exascale deep learning for climate analytics"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "amad adversarial multiscale anomaly detection on high dimensional and time evolving categorical data"):
  with members:
    - "neural related work summarization with a joint context driven attention mechanism"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"
    - "adversarially learned anomaly detection"
    - "edge2vec representation learning using edge semantics for biomedical knowledge discovery"
    - "generative adversarial networks an overview"
    - "novelty detection with gan"
    - "anomaly detection using one class 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)