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: "a new coding paradigm for the primitive relay channel"
  Abstract: We present a coding paradigm that provides a new achievable rate for the primitive relay channel by combining compress-and-forward and decode-and-forward with a chaining construction. #R##N#In the primitive relay channel model, the source broadcasts a message to the relay and to the destination; and the relay facilitates this communication by sending an additional message to the destination through a separate channel. Two well-known coding approaches for this setting are decode-and-forward and compress-and-forward: in the former, the relay decodes the message and sends some of the information to the destination; in the latter, the relay does not attempt to decode, but it sends a compressed description of the received sequence to the destination via Wyner-Ziv coding. #R##N#In our scheme, we transmit over pairs of blocks and we use compress-and-forward for the first block and decode-and-forward for the second. In particular, in the first block, the relay does not attempt to decode and it sends only a part of the compressed description of the received sequence; in the second block, the relay decodes the message and sends this information plus the remaining part of the compressed sequence relative to the first block. As a result, we strictly outperform both compress-and-forward and decode-and-forward. Furthermore, this paradigm can be implemented with a low-complexity polar coding scheme that has the typical attractive features of polar codes, i.e., quasi-linear encoding/decoding complexity and super-polynomial decay of the error probability. Throughout the paper we consider as a running example the special case of the erasure relay channel and we compare the rates achievable by our proposed scheme with the existing upper and lower bounds.

Connected through hyperedge HE-1 (induced by source paper "the arbitrarily varying gaussian relay channel with sender frequency division"):
  with members:
    - "the arbitrarily varying relay channel"
    - "arbitrarily varying wiretap channels with type constrained states"
    - "cut set bound is loose for gaussian relay networks"
    - "capacity approximations for gaussian relay 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)