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 binary detection with lossy data compression"
  Abstract: Consider the problem where a statistician in a two-node system receives rate-limited information from a transmitter about marginal observations of a memoryless process generated from two possible distributions. Using its own observations, this receiver is required to first identify the legitimacy of its sender by declaring the joint distribution of the process, and then depending on such authentication it generates the adequate reconstruction of the observations satisfying an average per-letter distortion. The performance of this setup is investigated through the corresponding rate-error-distortion region describing the trade-off between: the communication rate, the error exponent induced by the detection and the distortion incurred by the source reconstruction. In the special case of testing against independence, where the alternative hypothesis implies that the sources are independent, the optimal rate-error-distortion region is characterized. An application example to binary symmetric sources is given subsequently and the explicit expression for the rate-error-distortion region is provided as well. The case of "general hypotheses" is also investigated. A new achievable rate-error-distortion region is derived based on the use of non-asymptotic binning, improving the quality of communicated descriptions. Further improvement of performance in the general case is shown to be possible when the requirement of source reconstruction is relaxed, which stands in contrast to the case of general hypotheses.

Connected through hyperedge HE-1 (induced by source paper "privacy aware distributed hypothesis testing"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "distributed hypothesis testing with privacy constraints"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "distributed hypothesis testing with a privacy constraint"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "distributed hypothesis testing with privacy constraints"
    - "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"

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"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "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 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"
    - "neyman pearson test for zero rate multiterminal hypothesis testing"
    - "collaborative distributed hypothesis testing"
    - "unequal error protection an information theoretic perspective"

Connected through hyperedge HE-5 (induced by source paper "non parametric message important measure storage code design and transmission planning for big data"):
  with members:
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "message importance measure and its application to minority subset detection in big data"
    - "wireless communications in the era of big data"
    - "renyi divergence and kullback leibler divergence"
    - "the deterministic time linearity of service provided by fading channels"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "on the distortion snr exponent of some layered transmission schemes"

Connected through hyperedge HE-6 (induced by source paper "on binary distributed hypothesis testing"):
  with members:
    - "distributed structure joint expurgation for the multiple access channel"
    - "on the reliability function of variable rate slepian wolf coding"
    - "are slepian wolf rates necessary for distributed parameter estimation"
    - "reliability in source coding with side information"

Connected through hyperedge HE-7 (induced by source paper "distributed k means over compressed binary data"):
  with members:
    - "compressive k means"
    - "compression for quadratic similarity queries"
    - "randomized dimensionality reduction for k means clustering"

Connected through hyperedge HE-8 (induced by source paper "collaborative information bottleneck"):
  with members:
    - "distributed information theoretic biclustering"
    - "the three terminal interactive lossy source coding problem"
    - "deep learning with elastic averaging sgd"
    - "representation learning a review and new perspectives"
    - "multiterminal source coding under logarithmic loss"
    - "information theoretic proofs of entropy power inequalities"
    - "cascade triangular and two way source coding with degraded side information at the second user"
    - "two way source coding with a helper"

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)