human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a locality based approach for coded computation"):
  Abstract: Modern distributed computation infrastructures are often plagued by unavailabilities such as failing or slow servers. These unavailabilities adversely affect the tail latency of computation in distributed infrastructures. The simple solution of replicating computation entails significant resource overhead. Coded computation has emerged as a resource-efficient alternative, wherein multiple units of data are encoded to create parity units and the function to be computed is applied to each of these units on distinct servers. A decoder can use the available function outputs to decode the unavailable ones. Existing coded computation approaches are resource efficient only for simple variants of linear functions such as multilinear, with even the class of low degree polynomials requiring the same multiplicative overhead as replication for practically relevant straggler tolerance. #R##N#In this paper, we present a new approach to model coded computation via the lens of locality of codes. We introduce a generalized notion of locality, denoted computational locality, building upon the locality of an appropriately defined code. We show that computational locality is equivalent to the required number of workers for coded computation and leverage results from the well-studied locality of codes to design coded computation schemes. We show that recent results on coded computation of multivariate polynomials can be derived using local recovering schemes for Reed-Muller codes. We present coded computation schemes for multivariate polynomials that adaptively exploit locality properties of input data-- an inadmissible technique under existing frameworks. These schemes require fewer workers than the lower bound under existing coded computation frameworks, showing that the existing multiplicative overhead on the number of servers is not fundamental for coded computation of nonlinear functions.

Includes members:
  Member "entangled polynomial codes for secure private and batch distributed matrix multiplication breaking the cubic barrier":
    also connected through other hyperedges:
      (none)
  Member "slack squeeze coded computing for adaptive straggler mitigation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coded computing for boolean functions")
  Member "timely throughput optimal coded computing over cloud networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coded computing for boolean functions")
  Member "train where the data is a case for bandwidth efficient coded training":
    also connected through other hyperedges:
      (none)
  Member "collage inference tolerating stragglers in distributed neural network inference using coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parity models a general framework for coding based resilience in ml inference")
  Member "erasurehead distributed gradient descent without delays using approximate gradient coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "numerically stable binary gradient coding")
  Member "cross subspace alignment codes for coded distributed batch computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "hierarchical coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed task replication for vehicular edge computing performance analysis and learning based algorithm")

Question: Please tell me which class the source paper 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)