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: "consistent algorithms for multiclass classification with a reject option"
  Abstract: We consider the problem of $n$-class classification ($n\geq 2$), where the classifier can choose to abstain from making predictions at a given cost, say, a factor $\alpha$ of the cost of misclassification. Designing consistent algorithms for such $n$-class classification problems with a `reject option' is the main goal of this paper, thereby extending and generalizing previously known results for $n=2$. We show that the Crammer-Singer surrogate and the one vs all hinge loss, albeit with a different predictor than the standard argmax, yield consistent algorithms for this problem when $\alpha=\frac{1}{2}$. More interestingly, we design a new convex surrogate that is also consistent for this problem when $\alpha=\frac{1}{2}$ and operates on a much lower dimensional space ($\log(n)$ as opposed to $n$). We also generalize all three surrogates to be consistent for any $\alpha\in[0, \frac{1}{2}]$.

Connected through hyperedge HE-1 (induced by source paper "aleatoric and epistemic uncertainty in machine learning a tutorial introduction"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "epistemic uncertainty sampling"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms for set valued prediction in multi class classification"):
  with members:
    - "billion scale similarity search with gpus"
    - "lshtc a benchmark for large scale text classification"
    - "learning reductions that really work"
    - "very deep convolutional networks for large scale image recognition"
    - "conditional probability tree estimation analysis and algorithms"
    - "optimizing f measure a tale of two approaches"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-3 (induced by source paper "a general theory for structured prediction with smooth convex surrogates"):
  with members:
    - "sharp analysis of learning with discrete losses"
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "exponential convergence of testing error for stochastic gradient methods"
    - "a consistent regularization approach for structured prediction"
    - "on the consistency of ordinal regression methods"
    - "exponential family graph matching and ranking"

Connected through hyperedge HE-4 (induced by source paper "convex calibration dimension for multiclass loss matrices"):
  with members:
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"

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)