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: "distributed hypothesis testing with privacy constraints"
  Abstract: We revisit the distributed hypothesis testing (or hypothesis testing with communication constraints) problem from the viewpoint of privacy. Instead of observing the raw data directly, the transmitter observes a sanitized or randomized version of it. We impose an upper bound on the mutual information between the raw and randomized data. Under this scenario, the receiver, which is also provided with side information, is required to make a decision on whether the null or alternative hypothesis is in effect. We first provide a general lower bound on the type-II exponent for an arbitrary pair of hypotheses. Next, we show that if the distribution under the alternative hypothesis is the product of the marginals of the distribution under the null (i.e., testing against independence), then the exponent is known exactly. Moreover, we show that the strong converse property holds. Using ideas from Euclidean information theory, we also provide an approximate expression for the exponent when the communication rate is low and the privacy level is high. Finally, we illustrate our results with a binary and a Gaussian example.

Connected through hyperedge HE-1 (induced by source paper "privacy aware distributed hypothesis testing"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "technical privacy metrics a systematic survey"
    - "distributed hypothesis testing based on unequal error protection codes"
    - "privacy against a hypothesis testing adversary"
    - "on the reliability function of distributed hypothesis testing under optimal detection"
    - "distributed hypothesis testing over discrete memoryless channels"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "on binary distributed hypothesis testing"

Connected through hyperedge HE-2 (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 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"
    - "distributed hypothesis testing over noisy channels"

Connected through hyperedge HE-3 (induced by source paper "distributed detection with empirically observed statistics"):
  with members:
    - "second order asymptotically optimal statistical classification"
    - "anonymous heterogeneous distributed detection optimal decision rules error exponents and the price of anonymity"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "asymptotically optimal matching of multiple sequences to source distributions and training sequences"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "privacy against statistical inference"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-4 (induced by source paper "distributed hypothesis testing with a privacy constraint"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "technical privacy metrics a systematic survey"
    - "distributed hypothesis testing based on unequal error protection codes"
    - "privacy against a hypothesis testing adversary"
    - "on the reliability function of distributed hypothesis testing under optimal detection"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "on binary distributed hypothesis testing"
    - "privacy utility tradeoffs under constrained data release mechanisms"

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)