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 of m ary relay trees with non binary message alphabets"
  Abstract: We study the detection performance of $M$-ary relay trees, where only the leaves of the tree represent sensors making measurements. The root of the tree represents the fusion center which makes an overall detection decision. Each of the other nodes is a relay node which aggregates $M$ messages sent by its child nodes into a new compressed message and sends the message to its parent node. Building on previous work on the detection performance of $M$-ary relay trees with binary messages, in this paper we study the case of non-binary relay message alphabets. We characterize the exponent of the error probability with respect to the message alphabet size $\mathcal D$, showing how the detection performance increases with $\mathcal D$. Our method involves reducing a tree with non-binary relay messages into an equivalent higher-degree tree with only binary messages.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "detection performance in balanced binary relay trees with node and link failures"):
  with members:
    - "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-3 (induced by source paper "submodularity and optimality of fusion rules in balanced binary relay trees"):
  with members:
    - "subexponential convergence for information aggregation on regular trees"
    - "error probability bounds for balanced binary relay 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)