human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "causal relay networks"):
  Abstract: In this paper, we study causal discrete-memoryless relay networks (DMRNs). The network consists of multiple nodes, each of which can be a source, relay, and/or destination. In the network, there are two types of relays, i.e., relays with one sample delay (strictly causal) and relays without delay (causal) whose transmit signal depends not only on the past received symbols but also on the current received symbol. For this network, we derive two new cut-set bounds, one when the causal relays have their own messages and the other when not. Using examples of a causal vector Gaussian two-way relay channel and a causal vector Gaussian relay channel, we show that the new cut-set bounds can be achieved by a simple amplify-and-forward type relaying. Our result for the causal relay channel strengthens the previously known capacity result for the same channel by El Gamal, Hassanpour, and Mammen.

Includes members:
  Member "information networks with in block memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified inner bound for the two receiver memoryless broadcast channel with channel state and message side information")
  Member "capacity of strong and very strong gaussian interference relay without delay channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "instantaneous relaying optimal strategies and interference neutralization")
  Member "on the capacity of the noncausal relay channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal power allocation strategies in full duplex relay networks")

Question: Please tell me which class the source paper 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)