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: "autonomous deep learning continual learning approach for dynamic environments"
  Abstract: The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-depth structure which is capable of achieving a trade-off between plasticity and stability. Network significance (NS) formula is proposed to drive the hidden nodes growing and pruning mechanism. Drift detection scenario (DDS) is put forward to signal distributional changes in data streams which induce the creation of a new hidden layer. Maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers. The efficacy of ADL is numerically validated under the prequential test-then-train procedure in lifelong environments using nine popular data stream problems. The numerical results demonstrate that ADL consistently outperforms recent continual learning methods while characterizing the automatic construction of network structures.

Connected through hyperedge HE-1 (induced by source paper "devdan deep evolving denoising autoencoder"):
  with members:
    - "online tool condition monitoring based on parsimonious ensemble"
    - "an online rfid localization in the manufacturing shopfloor"
    - "evolving ensemble fuzzy classifier"
    - "deep learning for iot big data and streaming analytics a survey"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "stochastic configuration networks fundamentals and algorithms"
    - "online deep learning learning deep neural networks on the fly"

Connected through hyperedge HE-2 (induced by source paper "automatic construction of multi layer perceptron network from streaming examples"):
  with members:
    - "an empirical study of example forgetting during deep neural network learning"
    - "evolving ensemble fuzzy classifier"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "online deep learning learning deep neural networks on the fly"
    - "gradient episodic memory for continual learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised deep learning approach in streaming environments"):
  with members:
    - "devdan deep evolving denoising autoencoder"
    - "mixmatch a holistic approach to semi supervised learning"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-4 (induced by source paper "atl autonomous knowledge transfer from many streaming processes"):
  with members:
    - "deep reconstruction classification networks for unsupervised domain adaptation"

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)