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 the capacity of cloud radio access networks with oblivious relaying"
  Abstract: We study the transmission over a network in which users send information to a remote destination through relay nodes that are connected to the destination via finite-capacity error-free links, i.e., a cloud radio access network. The relays are constrained to operate without knowledge of the users' codebooks, i.e., they perform oblivious processing. The destination, or central processor, however, is informed about the users' codebooks. We establish a single-letter characterization of the capacity region of this model for a class of discrete memoryless channels in which the outputs at the relay nodes are independent given the users' inputs. We show that both relaying \`a-la Cover-El Gamal, i.e., compress-and-forward with joint decompression and decoding, and "noisy network coding", are optimal. The proof of the converse part establishes, and utilizes, connections with the Chief Executive Officer (CEO) source coding problem under logarithmic loss distortion measure. Extensions to general discrete memoryless channels are also investigated. In this case, we establish inner and outer bounds on the capacity region. For memoryless Gaussian channels within the studied class of channels, we characterize the capacity region when the users are constrained to time-share among Gaussian codebooks. Furthermore, we also discuss the suboptimality of separate decompression-decoding and the role of time-sharing.

Connected through hyperedge HE-1 (induced by source paper "joint user selection power allocation and precoding design with imperfect csit for multi cell mu mimo downlink systems"):
  with members:
    - "optimal pilot and payload power control in single cell massive mimo systems"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "massive mimo with imperfect channel covariance information"
    - "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "cell free massive mimo versus small cells"
    - "a rate splitting strategy for massive mimo with imperfect csit"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

Connected through hyperedge HE-2 (induced by source paper "on the capacity regions of cloud radio access networks with limited orthogonal fronthaul"):
  with members:
    - "integer forcing architectures for uplink cloud radio access networks"
    - "generalized compression strategy for the downlink cloud radio access network"
    - "the capacity of the relay channel solution to cover s problem in the gaussian case"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "an uplink downlink duality for cloud radio access network"
    - "on achievability for downlink cloud radio access networks with base station cooperation"
    - "capacity bounds for diamond networks with an orthogonal broadcast channel"

Connected through hyperedge HE-3 (induced by source paper "vector gaussian ceo problem under logarithmic loss and applications"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss"
    - "chaining mutual information and tightening generalization bounds"
    - "on the universality of the logistic loss function"
    - "minimax learning for remote prediction"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "universality of logarithmic loss in lossy compression"
    - "a generalization of blahut arimoto algorithm to compute rate distortion regions of multiterminal source coding under logarithmic loss"

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)