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: "subexponential convergence for information aggregation on regular trees"
  Abstract: We consider the decentralized binary hypothesis testing problem on trees of bounded degree and increasing depth. For a regular tree of depth t and branching factor k>=2, we assume that the leaves have access to independent and identically distributed noisy observations of the 'state of the world' s. Starting with the leaves, each node makes a decision in a finite alphabet M, that it sends to its parent in the tree. Finally, the root decides between the two possible states of the world based on the information it receives. #R##N#We prove that the error probability vanishes only subexponentially in the number of available observations, under quite general hypotheses. More precisely the case of binary messages, decay is subexponential for any decision rule. For general (finite) message alphabet M, decay is subexponential for 'node-oblivious' decision rules, that satisfy a mild irreducibility condition. In the latter case, we propose a family of decision rules with close-to-optimal asymptotic behavior.

Connected through hyperedge HE-1 (induced by source paper "asynchronous majority dynamics in preferential attachment trees"):
  with members:
    - "stabilizing consensus with many opinions"
    - "reaching consensus via non bayesian asynchronous learning in social networks"
    - "learning in hierarchical social networks"

Connected through hyperedge HE-2 (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"
    - "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 "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"
    - "error probability bounds for balanced binary relay trees"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "detection performance of m ary relay trees with non binary message alphabets"):
  with members:
    - "error probability bounds for m ary relay trees"
    - "error probability bounds for binary relay trees with crummy sensors"
    - "error probability bounds for balanced binary relay trees"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-7 (induced by source paper "error probability bounds for m ary relay trees"):
  with members:
    - "error probability bounds for binary relay trees with crummy sensors"
    - "error probability bounds for balanced binary relay trees"
    - "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.MA(Multiagent Systems)