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: "open category detection with pac guarantees"
  Abstract: Open category detection is the problem of detecting "alien" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a "clean" training set that contains only the target categories of interest and an unlabeled "contaminated" training set that contains a fraction $\alpha$ of alien examples. Under the assumption that we know an upper bound on $\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements.

Connected through hyperedge HE-1 (induced by source paper "utilizing network properties to detect erroneous inputs"):
  with members:
    - "looking ahead anticipating pedestrians crossing with future frames prediction"
    - "adversarial examples are a natural consequence of test error in noise"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "natural adversarial examples"
    - "selectivenet a deep neural network with an integrated reject option"
    - "improved adversarial robustness by reducing open space risk via tent activations"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"

Connected through hyperedge HE-2 (induced by source paper "dropcluster a structured dropout for convolutional networks"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "confidence calibration for convolutional neural networks using structured dropout"
    - "pytorch an imperative style high performance deep learning library"
    - "dropblock a regularization method for convolutional networks"
    - "feature grouping as a stochastic regularizer for high dimensional structured data"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "deep anomaly detection with outlier exposure"
    - "google s cloud vision api is not robust to noise"

Connected through hyperedge HE-3 (induced by source paper "deep cnn based multi task learning for open set recognition"):
  with members:
    - "learning deep features for one class classification"
    - "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"
    - "the extreme value machine"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-4 (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 set domain adaptation by backpropagation"
    - "reducing network agnostophobia"
    - "learning factorized representations for open set domain adaptation"

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)