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: "convergence rates of active learning for maximum likelihood estimation"
  Abstract: An active learner is given a class of models, a large set of unlabeled examples, and the ability to interactively query labels of a subset of these examples; the goal of the learner is to learn a model in the class that fits the data well. #R##N#Previous theoretical work has rigorously characterized label complexity of active learning, but most of this work has focused on the PAC or the agnostic PAC model. In this paper, we shift our attention to a more general setting -- maximum likelihood estimation. Provided certain conditions hold on the model class, we provide a two-stage active learning algorithm for this problem. The conditions we require are fairly general, and cover the widely popular class of Generalized Linear Models, which in turn, include models for binary and multi-class classification, regression, and conditional random fields. #R##N#We provide an upper bound on the label requirement of our algorithm, and a lower bound that matches it up to lower order terms. Our analysis shows that unlike binary classification in the realizable case, just a single extra round of interaction is sufficient to achieve near-optimal performance in maximum likelihood estimation. On the empirical side, the recent work in ~\cite{Zhang12} and~\cite{Zhang14} (on active linear and logistic regression) shows the promise of this approach.

Connected through hyperedge HE-1 (induced by source paper "understanding goal oriented active learning via influence functions"):
  with members:
    - "on the accuracy of influence functions for measuring group effects"
    - "practical obstacles to deploying active learning"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-2 (induced by source paper "exploring connections between active learning and model extraction"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "robust physical world attacks on deep learning models"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "practical black box attacks against machine learning"
    - "near optimal active learning of halfspaces via query synthesis in the noisy setting"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-3 (induced by source paper "active learning in recommendation systems with multi level user preferences"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "budget constrained item cold start handling in collaborative filtering recommenders via optimal design"

Connected through hyperedge HE-4 (induced by source paper "on the relationship between data efficiency and error for uncertainty sampling"):
  with members:
    - "deep active learning over the long tail"

Connected through hyperedge HE-5 (induced by source paper "active and adaptive sequential learning"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-6 (induced by source paper "active regression via linear sample sparsification"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "tail bounds for volume sampled linear regression"
    - "relative error tensor low rank approximation"
    - "unbiased estimates for linear regression via volume sampling"
    - "fourier sparse interpolation without a frequency gap"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "sketching as a tool for numerical linear algebra"
    - "near optimal coresets for least squares regression"

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)