human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an unbiased risk estimator for learning with augmented classes"):
  Abstract: In this paper, we study the problem of learning with augmented classes (LAC), where new classes that do not appear in the training dataset might emerge in the testing phase. The mixture of known classes and new classes in the testing distribution makes the LAC problem quite challenging. Our discovery is that by exploiting cheap and vast unlabeled data, the testing distribution can be estimated in the training stage, which paves us a way to develop algorithms with nice statistical properties. Specifically, we propose an unbiased risk estimator over the testing distribution for the LAC problem, and further develop an efficient algorithm to perform the empirical risk minimization. Both asymptotic and non-asymptotic analyses are provided as theoretical guarantees. The efficacy of the proposed algorithm is also confirmed by experiments.

Includes members:
  Member "selectivenet a deep neural network with an integrated reject option":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "robust artificial intelligence and robust human organizations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "handling concept drift via model reuse")
  Member "the extreme value machine":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning")
  Member "classification under streaming emerging new classes a solution using completely random trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "continual egocentric object recognition")
  Member "mixture proportion estimation via kernel embedding of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards mixture proportion estimation without irreducibility")

Question: Please tell me which class the source paper 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)