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 network coding for acyclic networks with delays"
  Abstract: Problems related to network coding for acyclic, instantaneous networks (where the edges of the acyclic graph representing the network are assumed to have zero-delay) have been extensively dealt with in the recent past. The most prominent of these problems include (a) the existence of network codes that achieve maximum rate of transmission, (b) efficient network code constructions, and (c) field size issues. In practice, however, networks have transmission delays. In network coding theory, such networks with transmission delays are generally abstracted by assuming that their edges have integer delays. Note that using enough memory at the nodes of an acyclic network with integer delays can effectively simulate instantaneous behavior, which is probably why only acyclic instantaneous networks have been primarily focused on thus far. In this work, we elaborate on issues ((a), (b) and (c) above) related to network coding for acyclic networks with integer delays, which have till now mostly been overlooked. We show that the delays associated with the edges of the network cannot be ignored, and in fact turn out to be advantageous, disadvantageous or immaterial, depending on the topology of the network and the problem considered i.e., (a), (b) or (c). In the process, we also show that for a single source multicast problem in acyclic networks (instantaneous and with delays), the network coding operations at each node can simply be limited to storing old symbols and coding them over a binary field. Therefore, operations over elements of larger fields are unnecessary in the network, the trade-off being that enough memory exists at the nodes and at the sinks, and that the sinks have more processing power.

Connected through hyperedge HE-1 (induced by source paper "precoding based network alignment using transform approach for acyclic networks with delay"):
  with members:
    - "precoding based network alignment for three unicast sessions"
    - "on the feasibility of precoding based network alignment for three unicast sessions"
    - "network coding for multiple unicasts an interference alignment approach"
    - "interference alignment and the degrees of freedom for the k user interference channel"

Connected through hyperedge HE-2 (induced by source paper "a transform approach to linear network coding for acyclic networks with delay"):
  with members:
    - "interference alignment and the degrees of freedom for the k user interference channel"

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)