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: "finite blocklength performance of sequential transmission over bsc with noiseless feedback"
  Abstract: In this paper, we consider the expected blocklength of variable-length coding over the binary symmetric channel (BSC) with noiseless feedback. Horstein first proposed a simple one-phase scheme to achieve the capacity of BSC. Naghshvar et al. used a novel extrinsic Jensen-Shannon (EJS) divergence in a sequential transmission scheme that maximizes EJS (MaxEJS) and provided a non-asymptotic upper bound on the expected blocklength for MaxEJS. Simulations in this paper show that MaxEJS provides lower expected blocklengths than the original Horstein scheme, but the non-asymptotic bound of Naghshvar et al. is loose enough that lies above the simulated performance of Horstein scheme for a BSC with a small crossover probability. This paper proposes a new expression for MaxEJS expected blocklength that is a tight approximation of simulated performance. This expression is developed by exploring a genie-aided decoder (GAD) whose expected blocklength will always be larger than MaxEJS and can be approximated by two random walks. We conjecture that even with these two approximations, the expression may still be an upper bound on blocklength as suggested by the simulation results.

Connected through hyperedge HE-1 (induced by source paper "low complexity algorithms for transmission of short blocks over the bsc with full feedback"):
  with members:
    - "variable length convolutional coding for short blocklengths with decision feedback"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "optimal feedback communication via posterior matching"

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)