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: "deep learning for unsupervised insider threat detection in structured cybersecurity data streams"
  Abstract: Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.

Connected through hyperedge HE-1 (induced by source paper "insider threat detection via hierarchical neural temporal point processes"):
  with members:
    - "insight into insiders and it a survey of insider threat taxonomies analysis modeling and countermeasures"
    - "learning temporal point processes via reinforcement learning"
    - "wasserstein learning of deep generative point process models"
    - "vews a wikipedia vandal early warning system"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "an anomaly contribution explainer for cyber security applications"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "feature importance measure for non linear learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "active learning for network intrusion detection"):
  with members:
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "recurrent neural network language models for open vocabulary event level cyber anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "optimizing space air ground integrated networks by artificial intelligence"):
  with members:
    - "energy efficient uav communication with trajectory optimization"

Connected through hyperedge HE-5 (induced by source paper "unsupervised machine learning for networking techniques applications and research challenges"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "an introduction to deep learning for the physical layer"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "iot data analytics using deep learning"
    - "from 4g to 5g self organized network management meets machine learning"
    - "why and how networks should run themselves"

Connected through hyperedge HE-6 (induced by source paper "recurrent neural network language models for open vocabulary event level cyber anomaly detection"):
  with members:
    - "character level language modeling with hierarchical recurrent neural networks"
    - "character based neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "gated feedback recurrent neural networks"
    - "a clockwork rnn"
    - "scalable recommendation with poisson factorization"

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)