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: "quantization of prior probabilities for collaborative distributed hypothesis testing"
  Abstract: This paper studies the quantization of prior probabilities, drawn from an ensemble, in distributed detection with data fusion by combination of binary local decisions. Design and performance equivalences between a team of N agents and a more powerful single agent are obtained. Effects of identical quantization and diverse quantization on mean Bayes risk are compared. It is shown that when agents using diverse quantizers interact to agree on a perceived common risk, the effective number quantization levels is increased. With this collaboration, optimal diverse regular quantization with K cells per quantizer performs as well as optimal identical quantization with N(K-1)+1 cells per quantizer. Similar results are obtained for the maximum Bayes risk error criterion.

Connected through hyperedge HE-1 (induced by source paper "beliefs and expertise in sequential decision making"):
  with members:
    - "strategic communication between prospect theoretic agents over a gaussian test channel"
    - "towards the design of prospect theory based human decision rules for hypothesis testing"
    - "distributed hypothesis testing with social learning and symmetric fusion"
    - "social teaching being informative vs being right in sequential decision making"

Connected through hyperedge HE-2 (induced by source paper "towards the design of prospect theory based human decision rules for hypothesis testing"):
  with members:
    - "reliable crowdsourcing for multi class labeling using coding theory"

Connected through hyperedge HE-3 (induced by source paper "the non regular ceo problem"):
  with members:
    - "optimal grouping for group minimax hypothesis testing"
    - "quantization of prior probabilities for hypothesis testing"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-4 (induced by source paper "bounded confidence opinion dynamics in a social network of bayesian decision makers"):
  with members:
    - "social teaching being informative vs being right in sequential decision making"
    - "quantization of prior probabilities for hypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "distributed hypothesis testing with social learning and symmetric fusion"):
  with members:
    - "keep ballots secret on the futility of social learning in decision making by voting"
    - "social teaching being informative vs being right in sequential decision making"

Connected through hyperedge HE-6 (induced by source paper "optimal grouping for group minimax hypothesis testing"):
  with members:
    - "theoretical bounds in minimax decentralized hypothesis testing"
    - "minimax optimal bounds for detectors based on estimated prior probabilities"
    - "quantization of prior probabilities for hypothesis testing"
    - "bregman voronoi diagrams properties algorithms and applications"

Connected through hyperedge HE-7 (induced by source paper "social teaching being informative vs being right in sequential decision making"):
  with members:
    - "keep ballots secret on the futility of social learning in decision making by voting"
    - "quickest detection with social learning interaction of local and global decision makers"
    - "quantization of prior probabilities for hypothesis testing"

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.IT(Information Theory)