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: "the extreme value machine"
  Abstract: It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function—ideally under an efficient incremental update mechanism. While good algorithms that assume inputs from a fixed set of classes exist,  e.g.  , artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental learning in the presence of unknown query classes. Existing algorithms take little to no distributional information into account when learning recognition functions and lack a strong theoretical foundation. We address this gap by formulating a novel, theoretically sound classifier—the Extreme Value Machine (EVM). The EVM has a well-grounded interpretation derived from statistical Extreme Value Theory (EVT), and is the first classifier to be able to perform nonlinear kernel-free variable bandwidth incremental learning. Compared to other classifiers in the same deep network derived feature space, the EVM is accurate and efficient on an established benchmark partition of the ImageNet dataset.

Connected through hyperedge HE-1 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "averaging weights leads to wider optima and better generalization"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "deep cnn based multi task learning for open set recognition"):
  with members:
    - "learning deep features for one class classification"
    - "open category detection with pac guarantees"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "and autoregressive novelty detectors"
    - "learning from noisy web data with category level supervision"
    - "classification reconstruction learning for open set recognition"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-3 (induced by source paper "integrating propositional and relational label side information for hierarchical zero shot image classification"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "learning a deep embedding model for zero shot learning"
    - "label embedding for image classification"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"):
  with members:
    - "security of deep learning methodologies challenges and opportunities"
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "model reuse attacks on deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deep learning for encrypted traffic classification an overview"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "are you tampering with my data"

Connected through hyperedge HE-5 (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"
    - "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"
    - "unsupervised representation learning by sorting sequences"

Connected through hyperedge HE-6 (induced by source paper "unsupervised out of distribution detection by maximum classifier discrepancy"):
  with members:
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "generative openmax for multi class open set classification"
    - "understanding deep learning requires rethinking generalization"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (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"
    - "classification under streaming emerging new classes a solution using completely random trees"
    - "mixture proportion estimation via kernel embedding of distributions"

Connected through hyperedge HE-8 (induced by source paper "upredict a user level profiler based predictive framework for single vm applications in multi tenant clouds"):
  with members:
    - "patterns in the chaos a study of performance variation and predictability in public iaas clouds"

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)