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: "neyman pearson test for zero rate multiterminal hypothesis testing"
  Abstract: The problem of zero-rate multiterminal hypothesis testing is revisited. A Neyman-Pearson-like test is proposed and its non-asymptotic performance is clarified; for short blocklength, it is numerically examined that the proposed test is superior to a previously known Hoeffding-like test proposed by Han-Kobayashi. For the large deviation regime, it is shown that our proposed test achieves the optimal trade-off between the type I and type II exponents shown by Han-Kobayashi. Among the class of symmetric (type based) testing schemes, when the type I error probability is non-vanishing, the proposed test is optimal up to the second-order term of the type II error exponent; the second-order term is characterized in terms of the variance of the projected relative entropy density. The information geometry method plays an important role in the analysis as well as the construction of the test.

Connected through hyperedge HE-1 (induced by source paper "distributed hypothesis testing cooperation and concurrent detection"):
  with members:
    - "optimal rate exponent region for a class of hypothesis testing against conditional independence problems"
    - "vector gaussian ceo problem under logarithmic loss and applications"
    - "distributed hypothesis testing with collaborative detection"
    - "distributed hypothesis testing with privacy constraints"
    - "distributed hypothesis testing with concurrent detections"
    - "on hypothesis testing against independence with multiple decision centers"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "hypothesis testing under maximal leakage privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "distributed simulation and distributed inference"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "test without trust optimal locally private distribution testing"
    - "sample optimal identity testing with high probability"
    - "the optimality of correlated sampling"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "testing shape restrictions of discrete distributions"
    - "testing identity of structured distributions"

Connected through hyperedge HE-3 (induced by source paper "on the reliability function of distributed hypothesis testing under optimal detection"):
  with members:
    - "on hypothesis testing against independence with multiple decision centers"
    - "on binary distributed hypothesis testing"
    - "distributed hypothesis testing over noisy channels"
    - "hypothesis testing over the two hop relay network"
    - "a large deviations approach to secure lossy compression"
    - "distributed binary detection with lossy data compression"
    - "collaborative distributed hypothesis testing"
    - "on the reliability function of variable rate slepian wolf coding"

Connected through hyperedge HE-4 (induced by source paper "distributed hypothesis testing with privacy constraints"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "on hypothesis testing against independence with multiple decision centers"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "distributed hypothesis testing over noisy channels"
    - "euclidean information theory of networks"
    - "utility privacy tradeoffs in databases an information theoretic approach"

Connected through hyperedge HE-5 (induced by source paper "distributed hypothesis testing over discrete memoryless channels"):
  with members:
    - "on the reliability function of distributed hypothesis testing under optimal detection"
    - "distributed hypothesis testing over noisy channels"
    - "hypothesis testing over the two hop relay network"
    - "distributed binary detection with lossy data compression"
    - "collaborative distributed hypothesis testing"
    - "unequal error protection an information theoretic perspective"

Connected through hyperedge HE-6 (induced by source paper "anonymous heterogeneous distributed detection optimal decision rules error exponents and the price of anonymity"):
  with members:
    - "amplification by shuffling from local to central differential privacy via anonymity"

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)