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: "submodularity and optimality of fusion rules in balanced binary relay trees"
  Abstract: We study the distributed detection problem in a balanced binary relay tree, where the leaves of the tree are sensors generating binary messages. The root of the tree is a fusion center that makes the overall decision. Every other node in the tree is a fusion node that fuses two binary messages from its child nodes into a new binary message and sends it to the parent node at the next level. We assume that the fusion nodes at the same level use the same fusion rule. We call a string of fusion rules used at different levels a fusion strategy. We consider the problem of finding a fusion strategy that maximizes the reduction in the total error probability between the sensors and the fusion center. We formulate this problem as a deterministic dynamic program and express the solution in terms of Bellman's equations. We introduce the notion of stringsubmodularity and show that the reduction in the total error probability is a stringsubmodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, is within a factor of the optimal strategy in terms of reduction in the total error probability.

Connected through hyperedge HE-1 (induced by source paper "string submodular functions with curvature constraints"):
  with members:
    - "communications inspired projection design with application to compressive sensing"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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"
    - "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-3 (induced by source paper "auditing active learning with outcome dependent query costs"):
  with members:
    - "efficient active learning of halfspaces an aggressive approach"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "importance weighted active learning"

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"
    - "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)