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: "encoding data for htm systems"
  Abstract: Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. In this white paper we describe how to encode data as Sparse Distributed Representations (SDRs) for use in HTM systems. We explain several existing encoders, which are available through the open source project called NuPIC, and we discuss requirements for creating encoders for new types of data.

Connected through hyperedge HE-1 (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"
    - "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"
    - "continuous online sequence learning with an unsupervised neural network model"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-2 (induced by source paper "formal analysis of htm spatial pooler performance under predefined operation conditions"):
  with members:
    - "continuous online sequence learning with an unsupervised neural network model"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "continuous online sequence learning with an unsupervised neural network model"):
  with members:
    - "lstm a search space odyssey"
    - "orthogonal rnns and long memory tasks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)