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 semi supervised learning using submodular functions"
  Abstract: We consider active, semi-supervised learning in an offline transductive setting. We show that a previously proposed error bound for active learning on undirected weighted graphs can be generalized by replacing graph cut with an arbitrary symmetric submodular function. Arbitrary non-symmetric submodular functions can be used via symmetrization. Different choices of submodular functions give different versions of the error bound that are appropriate for different kinds of problems. Moreover, the bound is deterministic and holds for adversarially chosen labels. We show exactly minimizing this error bound is NP-complete. However, we also introduce for any submodular function an associated active semi-supervised learning method that approximately minimizes the corresponding error bound. We show that the error bound is tight in the sense that there is no other bound of the same form which is better. Our theoretical results are supported by experiments on real data.

Connected through hyperedge HE-1 (induced by source paper "on the fairness of time critical influence maximization in social networks"):
  with members:
    - "group fairness in influence maximization"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "gaps in information access in social networks"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "fair allocation of indivisible public goods"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"

Connected through hyperedge HE-2 (induced by source paper "adaptivity in adaptive submodularity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "stochastic submodular cover with limited adaptivity"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"

Connected through hyperedge HE-3 (induced by source paper "semi supervised adversarial active learning on attributed graphs"):
  with members:
    - "attributed network embedding via subspace discovery"
    - "semi supervised learning based on generative adversarial network a comparison between good gan and bad gan approach"
    - "good semi supervised learning that requires a bad gan"
    - "active learning for graph embedding"
    - "improved techniques for training gans"
    - "semi supervised classification with graph convolutional networks"
    - "towards principled unsupervised learning"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "stochastic greedy closing the optimality gap in exact weak submodular maximization"):
  with members:
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "lazier than lazy greedy"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "graph based semi supervised active learning for edge flows"):
  with members:
    - "flow smoothing and denoising graph signal processing in the edge space"
    - "random walks on simplicial complexes and the normalized hodge 1 laplacian"
    - "hodge laplacians on graphs"
    - "algorithms for lipschitz learning on graphs"
    - "active semi supervised learning using sampling theory for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "learning from less data diversified subset selection and active learning in image classification tasks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "yolo9000 better faster stronger"
    - "matching networks for one shot learning"
    - "deepid3 face recognition with very deep neural networks"
    - "active transfer learning with zero shot priors reusing past datasets for future tasks"
    - "learning transferable features with deep adaptation networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "unconstrained submodular maximization with constant adaptive complexity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "do less get more streaming submodular maximization with subsampling"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "gradient methods for submodular maximization"

Connected through hyperedge HE-8 (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 learning on trees and graphs"

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)