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: "sampling constrained asynchronous communication how to sleep efficiently"
  Abstract: The minimum energy, and, more generally, the minimum cost, to transmit one bit of information has been recently derived for bursty communication when information is available infrequently at random times at the transmitter. Furthermore, it has been shown that even if the receiver is constrained to sample only a fraction $\rho\in (0,1]$ of the channel outputs, there is no capacity penalty. That is, for any strictly positive sampling rate $\rho>0$, the asynchronous capacity per unit cost is the same as under full sampling, i.e., when $\rho=1$. Moreover, there is no penalty in terms of decoding delay. #R##N#The above results are asymptotic in nature, considering the limit as the number $B$ of bits to be transmitted tends to infinity, while the sampling rate $\rho$ remains fixed. A natural question is then whether the sampling rate $\rho(B)$ can drop to zero without introducing a capacity (or delay) penalty compared to full sampling. We answer this question affirmatively. The main result of this paper is an essentially tight characterization of the minimum sampling rate. We show that any sampling rate that grows at least as fast as $\omega(1/B)$ is achievable, while any sampling rate smaller than $o(1/B)$ yields unreliable communication. The key ingredient in our improved achievability result is a new, multi-phase adaptive sampling scheme for locating transient changes, which we believe may be of independent interest for certain change-point detection problems.

Connected through hyperedge HE-1 (induced by source paper "second order asymptotics for communication under strong asynchronism"):
  with members:
    - "channel detection in coded communication"
    - "asynchronous communication capacity bounds and suboptimality of training"
    - "asynchronous capacity per unit cost"
    - "communication under strong asynchronism"
    - "optimal sequential frame synchronization"

Connected through hyperedge HE-2 (induced by source paper "channel detection in coded communication"):
  with members:
    - "quickest transient change detection under a sampling constraint"
    - "on zero rate error exponents of finite state channels with input dependent states"
    - "simplified erasure list decoding"
    - "list decoding random coding exponents and expurgated exponents"
    - "asymptotically optimal decision rules for joint detection and source coding"
    - "exact random coding error exponents of optimal bin index decoding"
    - "erasure list random coding error exponents are not universally achievable"
    - "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"

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)