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: "network control without csi using rateless codes for downlink cellular systems"
  Abstract: Wireless network scheduling and control techniques (e.g., opportunistic scheduling) rely heavily on access to Channel State Information (CSI). However, obtaining this information is costly in terms of bandwidth, time, and power, and could result in large overhead. Therefore, a critical question is how to optimally manage network resources in the absence of such information. To that end, we develop a cross-layer solution for downlink cellular systems with imperfect (and possibly no) CSI at the transmitter. We use rateless codes to resolve channel uncertainty. To keep the decoding complexity low, we explicitly incorporate time-average block-size constraints, and aim to maximize the system utility. The block-size of a rateless code is determined by both the network control decisions and the unknown CSI of many time slots. Therefore, unlike standard utility maximization problems, this problem can be viewed as a constrained partial observed Markov decision problem (CPOMDP), which is known to be hard due to the "curse of dimensionality." However, by using a modified Lyapunov drift method, we develop a dynamic network control scheme, which yields a total network utility within O(1/Lav) of utility-optimal point achieved by infinite block-size channel codes, where Lav is the enforced value of the time-average block-size of rateless codes. This opens the door of being able to trade complexity/delay for performance gains in the absence of accurate CSI. Our simulation results show that the proposed scheme improves the network throughput by up to 68% over schemes that use fixed-rate codes.

Connected through hyperedge HE-1 (induced by source paper "constant delay and constant feedback moving window network coding for wireless multicast design and asymptotic analysis"):
  with members:
    - "scheduling of multicast and unicast services under limited feedback by using rateless codes"
    - "the effect of block wise feedback on the throughput delay trade off in streaming"
    - "throughput of rateless codes over broadcast erasure channels"
    - "dynamic rate adaptation for improved throughput and delay in wireless network coded broadcast"
    - "three schemes for wireless coded broadcast to heterogeneous users"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"
    - "network coding meets tcp"
    - "effective delay control in online network coding"

Connected through hyperedge HE-2 (induced by source paper "scheduling of multicast and unicast services under limited feedback by using rateless codes"):
  with members:
    - "multiuser scheduling in a markov modeled downlink using randomly delayed arq feedback"
    - "de randomizing shannon the design and analysis of a capacity achieving rateless code"
    - "throughput of rateless codes over broadcast erasure channels"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"
    - "zero rate feedback can achieve the empirical capacity"

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)