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: "error probability bounds for balanced binary relay trees"
  Abstract: We study the detection error probability associated with a balanced binary relay tree, where the leaves of the tree correspond to $N$ identical and independent detectors. The root of the tree represents a fusion center that makes the overall detection decision. Each of the other nodes in the tree are relay nodes that combine two binary messages to form a single output binary message. In this way, the information from the detectors is aggregated into the fusion center via the intermediate relay nodes. In this context, we describe the evolution of Type I and Type II error probabilities of the binary data as it propagates from the leaves towards the root. Tight upper and lower bounds for the total error probability at the fusion center as functions of $N$ are derived. These characterize how fast the total error probability converges to 0 with respect to $N$, even if the individual sensors have error probabilities that converge to 1/2.

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"
    - "detection performance in balanced binary relay trees with node and link failures"

Connected through hyperedge HE-2 (induced by source paper "whose opinion to follow in multihypothesis social learning a large deviations perspective"):
  with members:
    - "identifying infection sources and regions in large networks"
    - "controlled sensing for multihypothesis testing"
    - "on learning with finite memory"
    - "the value of feedback in decentralized detection"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-3 (induced by source paper "robust decentralized detection and social learning in tandem networks"):
  with members:
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "learning in hierarchical social networks"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "on learning with finite memory"

Connected through hyperedge HE-4 (induced by source paper "learning in hierarchical social networks"):
  with members:
    - "detection performance in balanced binary relay trees with node and link failures"
    - "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"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-5 (induced by source paper "detection performance in balanced binary relay trees with node and link failures"):
  with members:
    - "detection performance of m ary relay trees with non binary message alphabets"
    - "subexponential convergence for information aggregation on regular trees"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-6 (induced by source paper "hypothesis testing in feedforward networks with broadcast failures"):
  with members:
    - "learning in hierarchical social networks"
    - "on learning with finite memory"

Connected through hyperedge HE-7 (induced by source paper "rate of learning in hierarchical social networks"):
  with members:
    - "detection performance in balanced binary relay trees with node and link failures"
    - "submodularity and optimality of fusion rules in balanced binary relay trees"
    - "subexponential convergence for information aggregation on regular trees"
    - "quickest detection with social learning interaction of local and global decision makers"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-8 (induced by source paper "submodularity and optimality of fusion rules in balanced binary relay trees"):
  with members:
    - "detection performance of m ary relay trees with non binary message alphabets"
    - "subexponential convergence for information aggregation on regular trees"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "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)