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: "approximate ranking from pairwise comparisons"
  Abstract: A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defined as the probability that it beats a randomly chosen other item. Finding an exact ranking typically requires a prohibitively large number of comparisons, but in practice, approximate rankings are often adequate. Accordingly, we study the problem of finding approximate rankings from pairwise comparisons. We analyze an active ranking algorithm that counts the number of comparisons won, and decides whether to stop or which pair of items to compare next, based on confidence intervals computed from the data collected in previous steps. We show that this algorithm succeeds in recovering approximate rankings using a number of comparisons that is close to optimal up to logarithmic factors. We also present numerical results, showing that in practice, approximation can drastically reduce the number of comparisons required to estimate a ranking.

Connected through hyperedge HE-1 (induced by source paper "on sample complexity upper and lower bounds for exact ranking from noisy comparisons"):
  with members:
    - "from pac to instance optimal sample complexity in the plackett luce model"
    - "active ranking with subset wise preferences"
    - "adaptive sampling for coarse ranking"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "maximum selection and ranking under noisy comparisons"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-2 (induced by source paper "swapped face detection using deep learning and subjective assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "faceforensics learning to detect manipulated facial images"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "deepfakes a new threat to face recognition assessment and detection"
    - "fast face swap using convolutional neural networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "adaptive estimation for approximate k nearest neighbor computations"):
  with members:
    - "adaptive monte carlo optimization"
    - "implicit maximum likelihood estimation"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised evaluation metrics and learning criteria for non parallel textual transfer"):
  with members:
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"

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)