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 "noisy bayesian active learning"):
  Abstract: Consider the problem of noisy Bayesian active learning given a sample space, a finite label set, and a finite set of label generating functions from the sample space to the label set, also known as the function class. The objective is to identify the function in the function class that generates the labels using as few label queries as possible and with low probability of error despite possible corruption by independent noise. The key to achieving this objective relies on the selection of queries in a strategic and adaptive manner. The problem generalizes the problem of noisy generalized binary search [1]. This paper explores the connection between the above Bayesian active learning problem and the problem of information acquisition in which a Bayesian decision maker is responsible for dynamically collecting noisy observations so as to enhance her/his information in a speedy manner about an underlying phenomena of interest with a constraint on the probability of error. This view of the problem allows for developing a general lower bound on the expected number of queries to identify the function that generates the labels in terms of the observation noise statistics, the desired probability of error, and the cardinality of the function class. Furthermore, viewing the problem as an information acquisition problem enables a deterministic and Markov heuristic policy based on greedy maximization of Extrinsic Jensen-Shannon divergence [2]. The performance of this heuristic is compared with the state of the art strategies for noisy generalized binary search. In the case where the function class is sample-rich, it is shown that this heuristic is better than previous results and, in particular, matches the earlier proposed lower bound asymptotically.

Includes members:
  Member "active sequential hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity")
  Member "near optimal bayesian active learning with noisy observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active learning in the overparameterized and interpolating regime")
  Member "agnostic active learning without constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "a simple converse of burnashev s reliability function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the reliability function of discrete memoryless multiple access channel with feedback")
  Member "importance weighted active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive region based active learning")

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.IT(Information Theory)