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 optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
  Abstract: This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base stations (BSs) are connected to a cloud-computing-based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users’ messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in the C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Furthermore, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.

Connected through hyperedge HE-1 (induced by source paper "integer forcing architectures for uplink cloud radio access networks"):
  with members:
    - "achievability performance bounds for integer forcing source coding"
    - "green heterogeneous cloud radio access networks potential techniques performance trade offs and challenges"
    - "outage behavior of integer forcing with random unitary pre processing"
    - "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "successive integer forcing and its sum rate optimality"

Connected through hyperedge HE-2 (induced by source paper "making cell free massive mimo competitive with mmse processing and centralized implementation"):
  with members:
    - "ubiquitous cell free massive mimo communications"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "few bit csi acquisition for centralized cell free massive mimo with spatial correlation"
    - "can massive mimo support uplink intensive applications"
    - "on the performance of backhaul constrained cell free massive mimo with linear receivers"
    - "massive mimo has unlimited capacity"
    - "performance of cell free massive mimo systems with mmse and lsfd receivers"
    - "on the total energy efficiency of cell free massive mimo"

Connected through hyperedge HE-3 (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"
    - "on the capacity of cloud radio access networks with oblivious relaying"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "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-4 (induced by source paper "vector gaussian ceo problem under logarithmic loss"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss and applications"
    - "universality of logarithmic loss in lossy compression"
    - "justification of logarithmic loss via the benefit of side information"
    - "rate region of the vector gaussian ceo problem with the trace distortion constraint"
    - "secure source coding with a public helper"
    - "an outer bound for the vector gaussian ceo problem"
    - "multiterminal source coding under logarithmic loss"
    - "multiterminal source coding with an entropy based distortion measure"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "generalized compression strategy for the downlink cloud radio access network"):
  with members:
    - "hybrid data sharing and compression strategy for downlink cloud radio access network"
    - "an uplink downlink duality for cloud radio access network"
    - "on achievability for downlink cloud radio access networks with base station cooperation"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "distributed decode forward for relay networks"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"

Connected through hyperedge HE-7 (induced by source paper "successive wyner ziv coding for the binary ceo problem under logarithmic loss"):
  with members:
    - "successive wyner ziv coding for the binary ceo problem under log loss"
    - "analysis and code design for the binary ceo problem under logarithmic loss"
    - "remote source coding under gaussian noise dueling roles of power and entropy power"
    - "the ceo problem with rth power of difference and logarithmic distortions"
    - "the non regular ceo problem"
    - "how to achieve the capacity of asymmetric channels"
    - "multiterminal source coding under logarithmic loss"
    - "decentralized coding algorithms for distributed storage in wireless sensor networks"

Connected through hyperedge HE-8 (induced by source paper "full duplex cloud radio access network stochastic design and analysis"):
  with members:
    - "self interference in full duplex multi user mimo channels"
    - "cross layer design for downlink multihop cloud radio access networks with network coding"
    - "secrecy and energy efficiency in massive mimo aided heterogeneous c ran a new look at interference"
    - "echo state networks for proactive caching in cloud based radio access networks with mobile users"
    - "a stochastic analysis of network mimo systems"
    - "massive mimo enabled full duplex cellular networks"
    - "full duplex mimo small cell networks with interference cancellation"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"

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)