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: "classification under streaming emerging new classes a solution using completely random trees"
  Abstract: This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The SENC problem can be decomposed into three sub problems: detecting emerging new classes, classifying for known classes, and updating models to enable classification of instances of the new class and detection of more emerging new classes. The proposed method employs completely random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods.

Connected through hyperedge HE-1 (induced by source paper "continual egocentric object recognition"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "harmonious attention network for person re identification"
    - "the extreme value machine"
    - "lifelong learning with dynamically expandable networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "prototypical networks for few shot learning"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-2 (induced by source paper "multi stage deep classifier cascades for open world recognition"):
  with members:
    - "robust regression via online feature selection under adversarial data corruption"
    - "open world learning and application to product classification"
    - "learning deep features for one class classification"
    - "random warping series a random features method for time series embedding"
    - "progressive neural networks"
    - "open world visual recognition using knowledge graphs"
    - "similarity preserving representation learning for time series clustering"
    - "icarl incremental classifier and representation learning"

Connected through hyperedge HE-3 (induced by source paper "continual rare class recognition with emerging novel subclasses"):
  with members:
    - "open world learning and application to product classification"
    - "unseen class discovery in open world classification"
    - "doc deep open classification of text documents"
    - "continual learning with deep generative replay"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "fearnet brain inspired model for incremental learning"
    - "icarl incremental classifier and representation learning"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "an unbiased risk estimator for learning with augmented classes"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "robust artificial intelligence and robust human organizations"
    - "the extreme value machine"
    - "mixture proportion estimation via kernel embedding of distributions"

Connected through hyperedge HE-5 (induced by source paper "machine learning based delay aware uav detection and operation mode identification over encrypted wi fi traffic"):
  with members:
    - "device fingerprinting in wireless networks challenges and opportunities"
    - "flying objects detection from a single moving camera"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-6 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "open world learning and application to product classification"
    - "large scale long tailed recognition in an open world"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "bayesian semantic instance segmentation in open set world"
    - "learning a neural network based representation for open set recognition"
    - "open category detection with pac guarantees"
    - "open set domain adaptation by backpropagation"
    - "reducing network agnostophobia"

Connected through hyperedge HE-7 (induced by source paper "deep forest"):
  with members:
    - "distributed deep forest and its application to automatic detection of cash out fraud"
    - "thinet a filter level pruning method for deep neural network compression"
    - "relating cascaded random forests to deep convolutional neural networks for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"
    - "solving multiclass learning problems via error correcting output codes"

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.LG(Machine Learning)