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: "quickest transient change detection under a sampling constraint"
  Abstract: Given a discrete time series that undergoes a transient change in distribution from $P_0$ to $P_1$ at an unknown instant, this paper addresses the question of finding the minimum sampling rate needed in order to detect the change as efficiently as under full sampling. #R##N#The problem is cast into a Bayesian setup where the change, assumed to be of fixed known duration $n$, occurs randomly and uniformly within a time frame of size $A_n=2^{\alpha n}$ for some known uncertainty parameter $\alpha>0$. It is shown that, for any fixed $\alpha \in (0,D(P_1||P_0))$, as long as the sampling rate is of order $\omega(1/n)$ the change can be detected as quickly as under full sampling, in the limit of vanishing false-alarm probability. The delay, in this case, is some linear function of $n$. Conversely, if $\alpha>D(P_1||P_0) $ or if the sampling rate is $o(1/n)$ reliable detection is impossible---the false-alarm probability is bounded away from zero or the delay is $\Theta(2^{\alpha n})$. #R##N#This paper illustrates this result through a recently proposed asynchronous communication framework. Here, the receiver observes mostly pure background noise except for a brief period of time, starting at an unknown instant, when data is transmitted thereby inducing a local change in distribution at the receiver. For this model, capacity per unit cost (minimum energy to transmit one bit of information) and communication delay were characterized and shown to be unaffected by a sparse sampling at the receiver as long as the sampling rate is a non-zero constant. This paper strengthens this result and shows that it continues to hold even if the sampling rate tends to zero at a rate no faster than $ \omega(1/B)$, where $B$ denotes the number of transmitted message bits. Conversely, if the sampling rate decreases as $o(1/B)$, reliable communication is impossible.

Connected through hyperedge HE-1 (induced by source paper "channel detection in coded communication"):
  with members:
    - "sampling constrained asynchronous communication how to sleep efficiently"
    - "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)