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: "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
  Abstract: We consider a distributed computing framework where the distributed nodes have different communication capabilities, motivated by the heterogeneous networks in data centers and mobile edge computing systems. Following the structure of MapReduce, this framework consists of Map computation phase, Shuffle phase, and Reduce computation phase. The Shuffle phase allows distributed nodes to exchange intermediate values, in the presence of heterogeneous communication bottlenecks for different nodes (heterogeneous communication load constraints). Focusing on two-node and three-node (K=2, 3) distributed computing systems with heterogeneous communication load constraints, in this work we characterize the minimum total computation load, as well as the minimum worst-case computation load for some cases. The worst-case computation load depends on the computation load of a node with the heaviest job. Therefore, by minimizing the worst-case computation load it could potentially minimize the system latency. We show an interesting insight that, for some cases, there is a tradeoff between the minimum total computation load and the minimum worst-case computation load, in the sense that both cannot be achieved at the same time. The achievability schemes are proposed with careful design on the file assignment and data shuffling. Finally, beyond the cut-set bound, a novel converse is proposed using the proof by contradiction.

Connected through hyperedge HE-1 (induced by source paper "coded distributed computing with heterogeneous function assignments"):
  with members:
    - "cascaded coded distributed computing on heterogeneous networks"
    - "leveraging coding techniques for speeding up distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "on caching with more users than files"
    - "on the optimality of uncoded cache placement"

Connected through hyperedge HE-2 (induced by source paper "cascaded coded distributed computing on heterogeneous networks"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-3 (induced by source paper "wireless mapreduce distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "a new combinatorial design of coded distributed computing"

Connected through hyperedge HE-4 (induced by source paper "coded computing for distributed graph analytics"):
  with members:
    - "coded cooperative computation for internet of things"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "compressed coded distributed computing"
    - "fundamental limits of coded linear transform"
    - "fundamental resource trade offs for encoded distributed optimization"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "near optimal coded data shuffling for distributed learning"

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)