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: "capacity of a post channel with and without feedback"
  Abstract: We consider finite state channels where the state of the channel is its previous output. We refer to these as POST (Previous Output is the STate) channels. We first focus on POST($\alpha$) channels. These channels have binary inputs and outputs, where the state determines if the channel behaves as a $Z$ or an $S$ channel, both with parameter $\alpha$. %with parameter $\alpha.$ We show that the non feedback capacity of the POST($\alpha$) channel equals its feedback capacity, despite the memory of the channel. The proof of this surprising result is based on showing that the induced output distribution, when maximizing the directed information in the presence of feedback, can also be achieved by an input distribution that does not utilize of the feedback. We show that this is a sufficient condition for the feedback capacity to equal the non feedback capacity for any finite state channel. We show that the result carries over from the POST($\alpha$) channel to a binary POST channel where the previous output determines whether the current channel will be binary with parameters $(a,b)$ or $(b,a)$. Finally, we show that, in general, feedback may increase the capacity of a POST channel.

Connected through hyperedge HE-1 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "continuous control with deep reinforcement learning"
    - "capacity and coding for the ising channel with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "computable upper bounds on the capacity of finite state channels"):
  with members:
    - "capacity of burst noise erasure channels with and without feedback and input cost"
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "dual capacity upper bounds for noisy runlength constrained channels"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "capacity and coding for the ising channel with feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-3 (induced by source paper "two way function computation"):
  with members:
    - "two way interference channel capacity how to have the cake and eat it too"
    - "feedback through overhearing"
    - "perfect output feedback in the two user decentralized interference channel"
    - "computation in multicast networks function alignment and converse theorems"
    - "two way interference channels"
    - "on network coding for sum networks"
    - "two way networks when adaptation is useless"
    - "wireless network information flow a deterministic approach"

Connected through hyperedge HE-4 (induced by source paper "the channel capacity of channelrhodopsin and other intensity driven signal transduction receptors"):
  with members:
    - "thermodynamic properties of molecular communication"
    - "shannon capacity of signal transduction for multiple independent receptors"
    - "capacity of diffusion based molecular communication with ligand receptors"

Connected through hyperedge HE-5 (induced by source paper "single letter expression of capacity for a class of channels with memory"):
  with members:
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "directed information on abstract spaces properties and variational equalities"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-6 (induced by source paper "information theory of molecular communication directions and challenges"):
  with members:
    - "shannon capacity of signal transduction for multiple independent receptors"
    - "capacity limits of diffusion based molecular timing channels"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "type based sign modulation for molecular communication"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "information friction and its implications on minimum energy required for communication"
    - "a perspective on future research directions in information theory"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"

Connected through hyperedge HE-7 (induced by source paper "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"):
  with members:
    - "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "directed information on abstract spaces properties and variational equalities"
    - "directed information on abstract spaces properties and extremum problems"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-8 (induced by source paper "capacity analysis of discrete energy harvesting channels"):
  with members:
    - "capacity of the energy harvesting channel with a finite battery"
    - "can feedback increase the capacity of the energy harvesting channel"
    - "a randomized approach to the capacity of finite state channels"
    - "binary energy harvesting channel with finite energy storage"
    - "awgn channel under time varying amplitude constraints with causal information at the transmitter"
    - "optimum transmission policies for battery limited energy harvesting nodes"
    - "optimal packet scheduling in an energy harvesting communication system"
    - "extension of the blahut arimoto algorithm for maximizing directed information"

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)