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: "s2 an efficient graph based active learning algorithm with application to nonparametric classification"
  Abstract: This paper investigates the problem of active learning for binary label prediction on a graph. We introduce a simple and label-efficient algorithm called S2 for this task. At each step, S2 selects the vertex to be labeled based on the structure of the graph and all previously gathered labels. Specifically, S2 queries for the label of the vertex that bisects the *shortest shortest* path between any pair of oppositely labeled vertices. We present a theoretical estimate of the number of queries S2 needs in terms of a novel parametrization of the complexity of binary functions on graphs. We also present experimental results demonstrating the performance of S2 on both real and synthetic data. While other graph-based active learning algorithms have shown promise in practice, our algorithm is the first with both good performance and theoretical guarantees. Finally, we demonstrate the implications of the S2 algorithm to the theory of nonparametric active learning. In particular, we show that S2 achieves near minimax optimal excess risk for an important class of nonparametric classification problems.

Connected through hyperedge HE-1 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff"):
  with members:
    - "graph powering and spectral robustness"
    - "a survey on theoretical advances of community detection in networks"
    - "connectivity in random annulus graphs and the geometric block model"
    - "active community detection a maximum likelihood approach"
    - "active learning for community detection in stochastic block models"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "exact recovery in the stochastic block model"
    - "active learning on trees and graphs"

Connected through hyperedge HE-2 (induced by source paper "active learning in the overparameterized and interpolating regime"):
  with members:
    - "two models of double descent for weak features"
    - "how do infinite width bounded norm networks look in function space"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "deep active learning for named entity recognition"
    - "cost effective active learning for deep image classification"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-3 (induced by source paper "active learning for graph neural networks via node feature propagation"):
  with members:
    - "simplifying graph convolutional networks"
    - "gradient descent finds global minima of deep neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "junction tree variational autoencoder for molecular graph generation"
    - "deep active learning for named entity recognition"
    - "active learning for graph embedding"
    - "neural message passing for quantum chemistry"
    - "a meta learning approach to one step active learning"

Connected through hyperedge HE-4 (induced by source paper "active learning in the geometric block model"):
  with members:
    - "graph powering and spectral robustness"
    - "connectivity in random annulus graphs and the geometric block model"
    - "community recovery in hypergraphs"
    - "the geometric block model"
    - "active learning for community detection in stochastic block models"
    - "active learning on trees and graphs"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "bayesian semi supervised learning with graph gaussian processes"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "hierarchical subquery evaluation for active learning on a graph"

Connected through hyperedge HE-6 (induced by source paper "active learning on weighted graphs using adaptive and non adaptive approaches"):
  with members:
    - "active semi supervised learning using sampling theory for graph signals"
    - "localized iterative methods for interpolation in graph structured data"
    - "active semi supervised learning using submodular functions"

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)