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: "tiny codes for guaranteeable delay"
  Abstract: Future 5G systems will need to support ultra-reliable low-latency communications scenarios. From a latency-reliability viewpoint, it is inefficient to rely on average utility-based system design. Therefore, we introduce the notion of guaranteeable delay which is the average delay plus three standard deviations of the mean. We investigate the trade-off between guaranteeable delay and throughput for point-to-point wireless erasure links with unreliable and delayed feedback, by bringing together signal flow techniques to the area of coding. We use tiny codes, i.e. sliding window by coding with just 2 packets, and design three variations of selective-repeat ARQ protocols, by building on the baseline scheme, i.e. uncoded ARQ, developed by Ausavapattanakun and Nosratinia: (i) Hybrid ARQ with soft combining at the receiver; (ii) cumulative feedback-based ARQ without rate adaptation; and (iii) Coded ARQ with rate adaptation based on the cumulative feedback. Contrasting the performance of these protocols with uncoded ARQ, we demonstrate that HARQ performs only slightly better, cumulative feedback-based ARQ does not provide significant throughput while it has better average delay, and Coded ARQ can provide gains up to about 40% in terms of throughput. Coded ARQ also provides delay guarantees, and is robust to various challenges such as imperfect and delayed feedback, burst erasures, and round-trip time fluctuations. This feature may be preferable for meeting the strict end-to-end latency and reliability requirements of future use cases of ultra-reliable low-latency communications in 5G, such as mission-critical communications and industrial control for critical control messaging.

Connected through hyperedge HE-1 (induced by source paper "adaptive causal network coding with feedback"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "complete mdp convolutional codes"
    - "throughput maximization for delay sensitive random access communication"
    - "analysis of feedback error in automatic repeat request"
    - "low delay random linear coding over a stream"
    - "the effect of block wise feedback on the throughput delay trade off in streaming"
    - "an elias bound on the bhattacharyya distance of codes for channels with a zero error capacity"
    - "on the delay advantage of coding in packet erasure networks"

Connected through hyperedge HE-2 (induced by source paper "low latency network adaptive error control for interactive streaming"):
  with members:
    - "adaptive causal network coding with feedback"
    - "optimal streaming codes for channels with burst and arbitrary erasures"
    - "rate optimal streaming codes for channels with burst and isolated erasures"
    - "congestion control using fec for conversational multimedia communication"
    - "the effect of block wise feedback on the throughput delay trade off in streaming"

Connected through hyperedge HE-3 (induced by source paper "adaptive causal network coding with feedback for multipath multi hop communications"):
  with members:
    - "adaptive causal network coding with feedback"
    - "low delay random linear coding over a stream"
    - "joint coding and scheduling optimization in wireless systems with varying delay sensitivities"
    - "on coding for reliable communication over packet networks"

Connected through hyperedge HE-4 (induced by source paper "minimum latency fec design with delayed feedback mathematical modeling and efficient algorithms"):
  with members:
    - "joint scheduling and coding for low in order delivery delay over lossy paths with delayed feedback"
    - "network coding meets tcp"
    - "online planning algorithms for pomdps"

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)