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: "joint task assignment and resource allocation for d2d enabled mobile edge computing"
  Abstract: With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer non-linear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Next, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design.

Connected through hyperedge HE-1 (induced by source paper "joint optimization of signal design and resource allocation in wireless d2d edge computing"):
  with members:
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "decentralized computation offloading for multi user mobile edge computing a deep reinforcement learning approach"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "multiuser resource allocation for mobile edge computation offloading"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "the interplay between massive mimo and underlaid d2d networking"
    - "on feasibility of interference alignment in mimo interference networks"
    - "on feasibility of interference alignment in mimo interference networks"

Connected through hyperedge HE-2 (induced by source paper "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"):
  with members:
    - "joint computation and communication cooperation for energy efficient mobile edge computing"
    - "wireless powered user cooperative computation in mobile edge computing systems"
    - "throughput maximization for uav enabled wireless powered communication networks"
    - "waveform design for wireless power transfer with limited feedback"
    - "exploiting massive d2d collaboration for energy efficient mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "uav enabled wireless power transfer trajectory design and energy optimization"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"

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)