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: "controlling recurrent neural networks by conceptors"
  Abstract: The human brain is a dynamical system whose extremely complex sensor-driven neural processes give rise to conceptual, logical cognition. Understanding the interplay between nonlinear neural dynamics and concept-level cognition remains a major scientific challenge. Here I propose a mechanism of neurodynamical organization, called conceptors, which unites nonlinear dynamics with basic principles of conceptual abstraction and logic. It becomes possible to learn, store, abstract, focus, morph, generalize, de-noise and recognize a large number of dynamical patterns within a single neural system; novel patterns can be added without interfering with previously acquired ones; neural noise is automatically filtered. Conceptors help explaining how conceptual-level information processing emerges naturally and robustly in neural systems, and remove a number of roadblocks in the theory and applications of recurrent neural networks.

Connected through hyperedge HE-1 (induced by source paper "the power of linear recurrent neural networks"):
  with members:
    - "analysing soccer games with clustering and conceptors"
    - "difference between memory and prediction in linear recurrent networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "training recurrent networks online without backtracking"
    - "learning input and recurrent weight matrices in echo state networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "continual learning with hypernetworks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "large scale adversarial representation learning"
    - "three scenarios for continual learning"
    - "high fidelity image generation with fewer labels"
    - "experience replay for continual learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"

Connected through hyperedge HE-3 (induced by source paper "conceptor debiasing of word representations evaluated on weat"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "continual learning for sentence representations using conceptors"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "gender bias in neural natural language processing"
    - "correcting the common discourse bias in linear representation of sentences using conceptors"

Connected through hyperedge HE-4 (induced by source paper "continual learning for sentence representations using conceptors"):
  with members:
    - "zero training sentence embedding via orthogonal basis"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "the rapidly changing landscape of conversational agents"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "toward continual learning for conversational agents"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-5 (induced by source paper "recognizing human internal states a conceptor based approach"):
  with members:
    - "the free play sandbox a methodology for the evaluation of social robotics and a dataset of social interactions"

Connected through hyperedge HE-6 (induced by source paper "meta continual learning"):
  with members:
    - "online learning of a memory for learning rates"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "learning gradient descent better generalization and longer horizons"
    - "continual learning with deep generative replay"
    - "encoder based lifelong learning"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-7 (induced by source paper "generalize symbolic knowledge with neural rule engine"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "marrying up regular expressions with neural networks a case study for spoken language understanding"
    - "symbolic priors for rnn based semantic parsing"
    - "object oriented neural programming oonp for document understanding"
    - "advances in pre training distributed word representations"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "harnessing deep neural networks with logic rules"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "unsupervised post processing of word vectors via conceptor negation"):
  with members:
    - "fully statistical neural belief tracking"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "all but the top simple and effective postprocessing for word representations"
    - "a network based end to end trainable task oriented dialogue system"
    - "enriching word vectors with subword information"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "towards universal paraphrastic sentence embeddings"

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)