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: "an achievable region for a general multi terminal network and the corresponding chain graph representation"
  Abstract: Random coding, along with various standard techniques such as coded time-sharing, rate-splitting, superposition coding, and binning, are traditionally used in obtaining achievable rate regions for multi-terminal networks. The error analysis of such an achievable scheme relies heavily on the properties of strongly joint typical sequences and on bounds of the cardinality of typical sets. In this work, we obtain an achievable rate region for a general (i.e. an arbitrary set of messages shared amongst encoding nodes, which transmit to arbitrary decoding nodes) memoryless, single-hop, multi-terminal network without feedback or cooperation by introducing a general framework and notation, and carefully generalizing the derivation of the error analysis. We show that this general inner bound may be obtained from a chain graph representation of the encoding operations. This graph representation captures the statistical relationship among codewords and allows one to readily obtain the rate bounds that define the achievable rate region. The proposed graph representation naturally leads to the derivation of all the achievable schemes that can be generated by combining classic random coding techniques for any memoryless network used without feedback or cooperation. We also re-derive a few achievable regions for classic multi-terminal networks, such as the multi-access channel, the broadcast channel, and the interference channel, to show how this new representation allows one to quickly consider the possible choices of encoding/decoding strategies for any given network and the distribution of messages among the encoders and decoders.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of the cognitive interference channel with a common cognitive message"):
  with members:
    - "inner and outer bounds for the gaussian cognitive interference channel and new capacity results"
    - "the capacity of the interference channel with a cognitive relay in very strong interference"
    - "capacity to within 3 bits for a class of gaussian interference channels with a cognitive relay"
    - "a new capacity result for the z gaussian cognitive interference channel"
    - "the capacity of the semi deterministic cognitive interference channel and its application to constant gap results for the gaussian channel"
    - "on the capacity of the interference channel with a cognitive relay"
    - "new results on the capacity of the gaussian cognitive interference channel"
    - "outer bounds for the interference channel with a cognitive relay"

Connected through hyperedge HE-2 (induced by source paper "the capacity of the semi deterministic cognitive interference channel with a common cognitive message and approximate capacity for the gaussian case"):
  with members:
    - "inner and outer bounds for the gaussian cognitive interference channel and new capacity results"
    - "new results on the capacity of the gaussian cognitive interference channel"
    - "cognitive interference channels with confidential messages"

Connected through hyperedge HE-3 (induced by source paper "a new random coding technique that generalizes superposition coding and binning"):
  with members:
    - "lecture notes on network information theory"
    - "capacity of symmetric k user gaussian very strong interference channels"

Connected through hyperedge HE-4 (induced by source paper "on the capacity of a general multiple access channel and of a cognitive network in the very strong interference regime"):
  with members:
    - "the capacity of the interference channel with a cognitive relay in very strong interference"

Connected through hyperedge HE-5 (induced by source paper "an achievable region for a general multi terminal network and its chain graph representation"):
  with members:
    - "interference networks with general message sets a random coding scheme"
    - "the capacity of the semi deterministic cognitive interference channel and its application to constant gap results for the gaussian channel"
    - "lecture notes on network information theory"
    - "new inner and outer bounds for the discrete memoryless cognitive interference channel and some capacity results"
    - "fundamental constraints on multicast capacity regions"
    - "cognitive interference channels with confidential messages"
    - "rate regions for relay broadcast channels"

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)