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 learning on trees and graphs"
  Abstract: We investigate the problem of active learning on a given tree whose nodes are assigned binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we characterize (up to constant factors) the optimal placement of queries so to minimize the mistakes made on the non-queried nodes. Our query selection algorithm is extremely efficient, and the optimal number of mistakes on the non-queried nodes is achieved by a simple and efficient mincut classifier. Through a simple modification of the query selection algorithm we also show optimality (up to constant factors) with respect to the trade-off between number of queries and number of mistakes on non-queried nodes. By using spanning trees, our algorithms can be efficiently applied to general graphs, although the problem of finding optimal and efficient active learning algorithms for general graphs remains open. Towards this end, we provide a lower bound on the number of mistakes made on arbitrary graphs by any active learning algorithm using a number of queries which is up to a constant fraction of the graph size.

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"
    - "s2 an efficient graph based active learning algorithm with application to nonparametric classification"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-2 (induced by source paper "alpine active link prediction using network embedding"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "activehne active heterogeneous network embedding"
    - "active learning for graph embedding"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (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"
    - "s2 an efficient graph based active learning algorithm with application to nonparametric classification"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "hs 2 active learning over hypergraphs"):
  with members:
    - "on the minimax misclassification ratio of hypergraph community detection"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "quadratic decomposable submodular function minimization"
    - "predicting positive and negative links with noisy queries theory practice"
    - "inhomogoenous hypergraph clustering with applications"
    - "higher order organization of complex networks"
    - "clustering with same cluster queries"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-5 (induced by source paper "on pairwise clustering with side information"):
  with members:
    - "see the tree through the lines the shazoo algorithm"
    - "online similarity prediction of networked data from known and unknown graphs"
    - "generalization bounds for metric and similarity learning"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-6 (induced by source paper "active nearest neighbor learning in metric spaces"):
  with members:
    - "exact lower bounds for the agnostic probably approximately correct pac machine learning model"
    - "maximum margin multiclass nearest neighbors"
    - "minimax analysis of active learning"
    - "adaptive metric dimensionality reduction"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-7 (induced by source paper "s2 an efficient graph based active learning algorithm with application to nonparametric classification"):
  with members:
    - "active clustering robust and efficient hierarchical clustering using adaptively selected similarities"

Connected through hyperedge HE-8 (induced by source paper "online similarity prediction of networked data from known and unknown graphs"):
  with members:
    - "see the tree through the lines the shazoo algorithm"
    - "near optimal algorithms for online matrix prediction"
    - "random spanning trees and the prediction of weighted graphs"
    - "generalization bounds for metric and similarity learning"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "regularization techniques for learning with matrices"
    - "exact matrix completion via convex optimization"

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)