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: "age of information scaling in large networks with hierarchical cooperation"
  Abstract: Given $n$ randomly located source-destination (S-D) pairs on a fixed area network that want to communicate with each other, we study the age of information with a particular focus on its scaling as the network size $n$ grows. We propose a three-phase transmission scheme that utilizes \textit{hierarchical cooperation} between users along with \textit{mega update packets} and show that an average age scaling of $O(n^{\alpha(h)}\log n)$ per-user is achievable where $h$ denotes the number of hierarchy levels and $\alpha(h) = \frac{1}{3\cdot2^h+1}$ which tends to $0$ as $h$ increases such that asymptotically average age scaling of the proposed scheme is $O(\log n)$. To the best of our knowledge, this is the best average age scaling result in a status update system with multiple S-D pairs.

Connected through hyperedge HE-1 (induced by source paper "who should google scholar update more often"):
  with members:
    - "optimal selective encoding for timely updates"
    - "trading off computation with transmission in status update systems"
    - "average peak age of information minimization in uav assisted iot networks"
    - "a reinforcement learning framework for optimizing age of information in rf powered communication systems"
    - "age of information aware cache updating with file and age dependent update durations"
    - "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"

Connected through hyperedge HE-2 (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"
    - "on timely channel coding with hybrid arq"
    - "timely distributed computation with stragglers"
    - "age of information in multicast networks with multiple update streams"

Connected through hyperedge HE-3 (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"
    - "on timely channel coding with hybrid arq"

Connected through hyperedge HE-4 (induced by source paper "closed form whittle s index enabled random access for timely status update"):
  with members:
    - "age of information in g g 1 1 systems age expressions bounds special cases and optimization"
    - "using erasure feedback for online timely updating with an energy harvesting sensor"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "information aging through queues a mutual information perspective"
    - "delay performance of multi antenna multicasting in wireless networks"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "age optimal sampling and transmission scheduling in multi source systems"
    - "age of information in multihop multicast networks"

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)