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: "broadcast age of information in csma ca based wireless networks"
  Abstract: We consider a wireless sensor network in which all the nodes wish to spread their updates over the network using CSMA/CA protocol. We investigate the age of information of the spreading process from a transmitter perspective, which is referred to as the \textit{broadcast age of information (BAoI)}. To be specific, BAoI is the age of the latest update successfully broadcasted to the one-hop neighbors of a node, and thus is suitable to measure the rapidity of the update spreading process. We establish an equivalent transmission model of the network by deriving the transmission probability and the collision probability of nodes. With this equivalent model, we then present the average BAoI of the network explicitly. Our results present the scaling laws of average BAoI with respect to node density and frame length, and are further illustrated through numerical results. As is shown, the average BAoI is increasing with node density and is convex in frame length, i.e., would be large when frame length is very small or very large.

Connected through hyperedge HE-1 (induced by source paper "age of information for multicast transmission with fixed and random deadlines in iot systems"):
  with members:
    - "age of information for computation intensive messages in mobile edge computing"
    - "average peak age of information minimization in uav assisted iot networks"
    - "economics of age of information management under network externalities"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "adaptive coding for information freshness in a two user broadcast erasure channel"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"
    - "reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks"

Connected through hyperedge HE-2 (induced by source paper "optimizing information freshness using low power status updates via sleep wake scheduling"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "energy efficient coded random access for the wireless uplink"
    - "minimizing the age of information in a csma environment"
    - "age optimal updates of multiple information flows"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "timely status update in massive iot systems decentralized scheduling for wireless uplinks"
    - "sampling for data freshness optimization non linear age functions"
    - "distributed scheduling algorithms for optimizing information freshness in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "optimal age energy trade off via sleep wake scheduling"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "energy efficient coded random access for the wireless uplink"
    - "minimizing the age of information in a csma environment"
    - "timely status update in massive iot systems decentralized scheduling for wireless uplinks"
    - "sampling for data freshness optimization non linear age functions"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "age optimal information updates in multihop 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)