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: "active ranking using pairwise comparisons"
  Abstract: This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of $n$ objects can be identified by standard sorting methods using $n log_2 n$ pairwise comparisons. We are interested in natural situations in which relationships among the objects may allow for ranking using far fewer pairwise comparisons. Specifically, we assume that the objects can be embedded into a $d$-dimensional Euclidean space and that the rankings reflect their relative distances from a common reference point in $R^d$. We show that under this assumption the number of possible rankings grows like $n^{2d}$ and demonstrate an algorithm that can identify a randomly selected ranking using just slightly more than $d log n$ adaptively selected pairwise comparisons, on average. If instead the comparisons are chosen at random, then almost all pairwise comparisons must be made in order to identify any ranking. In addition, we propose a robust, error-tolerant algorithm that only requires that the pairwise comparisons are probably correct. Experimental studies with synthetic and real datasets support the conclusions of our theoretical analysis.

Connected through hyperedge HE-1 (induced by source paper "learning from noisy similar and dissimilar data"):
  with members:
    - "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning from binary labels with instance dependent corruption"

Connected through hyperedge HE-2 (induced by source paper "preference based interactive multi document summarisation"):
  with members:
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "viana visual interactive annotation of argumentation"
    - "better rewards yield better summaries learning to summarise without references"
    - "improving abstraction in text summarization"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "counterfactual learning from human proofreading feedback for semantic parsing"
    - "can neural machine translation be improved with user feedback"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "constrained generative adversarial networks for interactive image generation"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "invertible conditional gans for image editing"
    - "is the deconvolution layer the same as a convolutional layer"
    - "conditional image generation with pixelcnn decoders"
    - "generative adversarial text to image synthesis"
    - "pixel recurrent neural networks"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-4 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-5 (induced by source paper "mlfriend interactive prediction task recommendation for event driven time series data"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "learning transferable architectures for scalable image recognition"
    - "hierarchical representations for efficient architecture search"

Connected through hyperedge HE-6 (induced by source paper "sorted top k in rounds"):
  with members:
    - "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "competitive analysis of the top k ranking problem"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "sorting from noisy information"

Connected through hyperedge HE-7 (induced by source paper "learning mixtures of plackett luce models from structured partial orders"):
  with members:
    - "a cost effective framework for preference elicitation and aggregation"
    - "learning mixtures of plackett luce models"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "data driven rank breaking for efficient rank aggregation"
    - "efficient probabilistic inference with partial ranking queries"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "agent prioritization for autonomous navigation"):
  with members:
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "multi context attention for human pose estimation"
    - "xgboost a scalable tree boosting system"
    - "learning to track at 100 fps with deep regression networks"
    - "learning to order things"

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)