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: "on timely channel coding with hybrid arq"
  Abstract: A status updating communication system is examined, in which a transmitter communicates with a receiver over a noisy channel. The goal is to realize timely delivery of fresh data over time, which is assessed by an age-of-information (AoI) metric. Channel coding is used to combat the channel errors, and feedback is sent to acknowledge updates' reception. In case decoding is unsuccessful, a hybrid ARQ protocol is employed, in which incremental redundancy (IR) bits are transmitted to enhance the decoding ability. This continues for some amount of time in case decoding remains unsuccessful, after which a new (fresh) status update is transmitted instead. In case decoding is successful, the transmitter has the option to idly wait for a certain amount of time before sending a new update. A general problem is formulated that optimizes the codeword and IR lengths for each update, and the waiting times, such that the long term average AoI is minimized. Stationary deterministic policies are investigated, in which the codeword and IR lengths are fixed for each update, and the waiting time is a deterministic function of the AoI. The optimal waiting policy is then derived, and is shown to have a threshold structure, in which the transmitter sends a new update only if the AoI grows above a certain threshold that is a function of the codeword and IR lengths. Choosing the codeword and IR lengths is discussed in the context of binary symmetric channels.

Connected through hyperedge HE-1 (induced by source paper "partial updates losing information for freshness"):
  with members:
    - "optimal selective encoding for timely updates"
    - "trading off computation with transmission in status update systems"
    - "reducing age of information for computation intensive messages via packet replacement"
    - "timely distributed computation with stragglers"
    - "age of information in multicast networks with multiple update streams"
    - "age of information for updates with distortion"
    - "using erasure feedback for online timely updating with an energy harvesting sensor"
    - "age of information with finite horizon and partial updates"

Connected through hyperedge HE-2 (induced by source paper "optimal selective encoding for timely updates"):
  with members:
    - "trading off computation with transmission in status update systems"
    - "reducing age of information for computation intensive messages via packet replacement"
    - "timely distributed computation with stragglers"
    - "age of information in multicast networks with multiple update streams"
    - "age of information in g g 1 1 systems age expressions bounds special cases and optimization"
    - "age of information for updates with distortion"
    - "using erasure feedback for online timely updating with an energy harvesting sensor"
    - "age of information with finite horizon and partial updates"

Connected through hyperedge HE-3 (induced by source paper "age of information with gilbert elliot servers and samplers"):
  with members:
    - "partial updates losing information for freshness"
    - "optimal selective encoding for timely updates"
    - "who should google scholar update more often"
    - "age of information with prioritized streams when to buffer preempted packets"
    - "age of information for discrete time queues"
    - "timely distributed computation with stragglers"
    - "age of information in multicast networks with multiple update streams"
    - "age of information for updates with distortion constant and age dependent distortion constraints"

Connected through hyperedge HE-4 (induced by source paper "timely distributed computation with stragglers"):
  with members:
    - "straggler mitigation at scale"
    - "trading off computation with transmission in status update systems"
    - "age of information with prioritized streams when to buffer preempted packets"
    - "sampling for remote estimation through queues age of information and beyond"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "age of information for computation intensive messages in mobile edge computing"
    - "age of information for discrete time queues"
    - "mds coding is better than replication for job completion times"

Connected through hyperedge HE-5 (induced by source paper "timely cloud computing preemption and waiting"):
  with members:
    - "sampling for remote estimation through queues age of information and beyond"
    - "how to price fresh data"
    - "reducing age of information for computation intensive messages via packet replacement"
    - "on peak age of information in data preprocessing enabled iot networks"
    - "minimizing age of information with soft updates"
    - "status updates in a multi stream m g 1 1 preemptive queue"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "when to preempt age of information minimization under link capacity constraint"

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)