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: "robust interactive learning"
  Abstract: In this paper we propose and study a generalization of the standard active-learning model where a more general type of query, class conditional query, is allowed. Such queries have been quite useful in applications, but have been lacking theoretical understanding. In this work, we characterize the power of such queries under two well-known noise models. We give nearly tight upper and lower bounds on the number of queries needed to learn both for the general agnostic setting and for the bounded noise model. We further show that our methods can be made adaptive to the (unknown) noise rate, with only negligible loss in query complexity.

Connected through hyperedge HE-1 (induced by source paper "diameter based interactive structure discovery"):
  with members:
    - "diameter based active learning"
    - "a general framework for robust interactive learning"
    - "interactive bayesian hierarchical clustering"
    - "equality of opportunity in supervised learning"
    - "clustering with same cluster queries"
    - "efficient learning of linear separators under bounded noise"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "local algorithms for interactive clustering"

Connected through hyperedge HE-2 (induced by source paper "an analysis of the value of information when exploring stochastic discrete multi armed bandits"):
  with members:
    - "analysis of agent expertise in ms pac man using value of information based policies"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "active classification with comparison queries"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "dimension coupling optimal active learning of halfspaces via query synthesis"
    - "clustering with same cluster queries"
    - "the optimal sample complexity of pac learning"
    - "beyond disagreement based agnostic active learning"
    - "a compression technique for analyzing disagreement based active learning"
    - "minimax analysis of active learning"
    - "learning using local membership queries"

Connected through hyperedge HE-4 (induced by source paper "active learning from imperfect labelers"):
  with members:
    - "search improves label for active learning"
    - "active learning from weak and strong labelers"
    - "beyond disagreement based agnostic active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-5 (induced by source paper "search improves label for active learning"):
  with members:
    - "efficient and parsimonious agnostic active learning"
    - "beyond disagreement based agnostic active learning"
    - "agnostic active learning without constraints"

Connected through hyperedge HE-6 (induced by source paper "deep value of information estimators for collaborative human machine information gathering"):
  with members:
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "end to end training of deep visuomotor policies"
    - "belief propagation for structured decision making"
    - "near optimal nonmyopic value of information in graphical models"
    - "finding approximate pomdp solutions through belief compression"

Connected through hyperedge HE-7 (induced by source paper "minimax analysis of active learning"):
  with members:
    - "a compression technique for analyzing disagreement based active learning"
    - "the power of localization for efficiently learning linear separators with noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "active learning using smooth relative regret approximations with applications"
    - "agnostic active learning without constraints"
    - "importance weighted active learning"

Connected through hyperedge HE-8 (induced by source paper "sparse illumination learning and transfer for single sample face recognition with image corruption and misalignment"):
  with members:
    - "collaborative representation based classification for face recognition"
    - "exact recovery of sparsely used dictionaries"
    - "fast l1 minimization algorithms for robust face recognition"

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)