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: "continuous online sequence learning with an unsupervised neural network model"
  Abstract: The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory is recently proposed as a theoretical framework for sequence learning in the cortex. In this paper, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable-order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average (ARIMA), feedforward neural networks: online sequential extreme learning machine (ELM), and recurrent neural networks: long short-term memory (LSTM) and echo-state networks (ESN), on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyper- parameters tuning. Therefore the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem, but is also applicable to a wide range of real-world problems such as discrete and continuous sequence prediction, anomaly detection, and sequence classification.

Connected through hyperedge HE-1 (induced by source paper "unsupervised progressive learning and the stam architecture"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "continual learning via neural pruning"
    - "three scenarios for continual learning"
    - "continual learning with tiny episodic memories"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-2 (induced by source paper "local unsupervised learning for image analysis"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "dense associative memory is robust to adversarial inputs"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "online representation learning with single and multi layer hebbian networks for image classification"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "adversarial feature learning"
    - "dense associative memory for pattern recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "problem adapted artificial intelligence for online network optimization"):
  with members:
    - "introduction to online convex optimization"
    - "experience driven networking a deep reinforcement learning based approach"
    - "artificial intelligence as an enabler for cognitive self organizing future networks"

Connected through hyperedge HE-4 (induced by source paper "hierarchical temporal memory using memristor networks a survey"):
  with members:
    - "feature extraction without learning in an analog spatial pooler memristive cmos circuit design of hierarchical temporal memory"
    - "on chip face recognition system design with memristive hierarchical temporal memory"
    - "a survey of memristive threshold logic circuits"
    - "hierarchical temporal memory based on spin neurons and resistive memory for energy efficient brain inspired computing"
    - "non volatile hierarchical temporal memory hardware for spatial pooling"

Connected through hyperedge HE-5 (induced by source paper "hardware based spatio temporal neural processing backend for imaging sensors towards a smart camera"):
  with members:
    - "reservoir computing using stochastic p bits"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "using spatial pooler of hierarchical temporal memory to classify noisy videos with predefined complexity"):
  with members:
    - "opencl accelerated object classification in video streams using spatial pooler of hierarchical temporal memory"
    - "formal analysis of htm spatial pooler performance under predefined operation conditions"
    - "encoding data for htm systems"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "deep learning in neural networks"
    - "beyond short snippets deep networks for video classification"
    - "action recognition using visual attention"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-7 (induced by source paper "htm mat an online prediction software toolbox based on cortical machine learning algorithm"):
  with members:
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "a review of 40 years of cognitive architecture research core cognitive abilities and practical applications"):
  with members:
    - "enabling basic normative hri in a cognitive robotic architecture"
    - "a roadmap towards machine intelligence"
    - "grid long short term memory"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
    - "symphony from synapses neocortex as a universal dynamical systems modeller using hierarchical temporal memory"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "multiple object recognition with visual attention"

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)