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: "memory augmented neural networks for predictive process analytics"
  Abstract: Process analytics involves a sophisticated layer of data analytics built over the traditional notion of process mining. The flexible execution of business process instances involves multiple critical decisions including what task to perform next and what resources to allocate to a task. In this paper, we explore the application of deep learning techniques for solving various process analytics related problems. Based on recent advances in the field we specifically look at memory-augmented neural networks (MANN)s and adapt the latest model to date, namely the Differential Neural Computer. We introduce two modifications to account for a variety of tasks in predictive process analytics: (i) separating the encoding phase and decoding phase, resulting dual controllers, one for each phase; (ii) implementing a write-protected policy for the memory during the decoding phase. We demonstrate the feasibility and usefulness of our approach by solving a number of common process analytics tasks such as next activity prediction, time to completion and suffix prediction. We also introduce the notion of MANN based process analytics recommendation machinery that once deployed can serve as an effective business process recommendation engine enabling organizations to answer various prescriptive process analytics related questions.Using real-world datasets, we benchmark our results against those obtained from the state-of-art methods. We show that MANNs based process analytics methods can acheive state-of-the-art performance and have a lot of value to offer for enterprise specific process anlaytics applications.

Connected through hyperedge HE-1 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory"):
  with members:
    - "learning to remember more with less memorization"
    - "dual control memory augmented neural networks for treatment recommendations"
    - "robust and scalable differentiable neural computer for question answering"
    - "rosetta large scale system for text detection and recognition in images"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "handwritten digit string recognition by combination of residual network and rnn ctc"
    - "implicit language model in lstm for ocr"
    - "reading scene text with attention convolutional sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "decay replay mining to predict next process events"):
  with members:
    - "tackling climate change with machine learning"
    - "process mining of programmable logic controllers input output event logs"
    - "deep learning a critical appraisal"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "automated discovery of process models from event logs review and benchmark"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "the internet of things meets business process management mutual benefits and challenges"

Connected through hyperedge HE-3 (induced by source paper "dream nap decay replay mining to predict next process activities"):
  with members:
    - "deep learning a critical appraisal"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "automated discovery of process models from event logs review and benchmark"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "the internet of things meets business process management mutual benefits and challenges"
    - "understanding deep learning requires rethinking generalization"
    - "time and activity sequence prediction of business process instances"

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.NE(Neural and Evolutionary Computing)