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: "detection performance in balanced binary relay trees with node and link failures"
  Abstract: We study the distributed detection problem in the context of a balanced binary relay tree, where the leaves of the tree correspond to N identical and independent sensors generating binary messages. The root of the tree is a fusion center making an overall decision. Every other node is a relay node that aggregates the messages received from its child nodes into a new message and sends it up toward the fusion center. We derive upper and lower bounds for the total error probability PN as explicit functions of N in the case where nodes and links fail with certain probabilities. These characterize the asymptotic decay rate of the total error probability as N goes to infinity. Naturally, this decay rate is not larger than that in the non-failure case, which is √N . However, we derive an explicit necessary and sufficient condition on the decay rate of the local failure probabilities pk (combination of node and link failure probabilities at each level) such that the decay rate of the total error probability in the failure case is the same as that of the non-failure case. More precisely, we show that logPN-1=Θ(√N) if and only if logpk-1=Ω(2k/2) .

Connected through hyperedge HE-1 (induced by source paper "average error probability in wireless sensor networks with imperfect sensing and communication for different decision rules"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"
    - "effects of relay selection strategies on the spectral efficiency of wireless systems with half and full duplex nodes"
    - "a survey on internet of things from industrial market perspective"
    - "throughput analysis of cognitive wireless networks with poisson distributed nodes based on location information"
    - "error probability bounds for balanced binary relay trees"

Connected through hyperedge HE-2 (induced by source paper "distributed inference in tree networks using coding theory"):
  with members:
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-3 (induced by source paper "massive mimo for wireless sensing with a coherent multiple access channel"):
  with members:
    - "detection in analog sensor networks with a large scale antenna fusion center"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "estimation in phase shift and forward wireless sensor networks"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-4 (induced by source paper "learning in hierarchical social networks"):
  with members:
    - "submodularity and optimality of fusion rules in balanced binary relay trees"
    - "detection performance of m ary relay trees with non binary message alphabets"
    - "rate of learning in hierarchical social networks"
    - "subexponential convergence for information aggregation on regular trees"
    - "error probability bounds for balanced binary relay trees"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-5 (induced by source paper "rate of learning in hierarchical social networks"):
  with members:
    - "submodularity and optimality of fusion rules in balanced binary relay trees"
    - "subexponential convergence for information aggregation on regular trees"
    - "error probability bounds for balanced binary relay trees"
    - "quickest detection with social learning interaction of local and global decision makers"
    - "data fusion trees for detection does architecture matter"

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)