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: "block diagonal coding for distributed computing with straggling servers"
  Abstract: We consider the distributed computing problem of multiplying a set of vectors with a matrix. For this scenario, Li et al. recently presented a unified coding framework and showed a fundamental tradeoff between computational delay and communication load. This coding framework is based on maximum distance separable (MDS) codes of code length proportional to the number of rows of the matrix, which can be very large. We propose a block-diagonal coding scheme consisting of partitioning the matrix into submatrices and encoding each submatrix using a shorter MDS code. We prove that, up to a level of partitioning, the proposed scheme does not incur any loss in terms of computational delay and communication load compared to the scheme by Li et al.. We further show that the assignment of coded matrix rows to servers to minimize the communication load can be formulated as an integer program with a nonlinear cost function, and propose an algorithm to solve it. Numerical results show that, for heavy partitioning, the proposed coding scheme entails only a slight increase in computational delay and/or communication load. Partitioning, on the other hand, allows the use of much shorter MDS codes, thus significantly lowering the decoding complexity.

Connected through hyperedge HE-1 (induced by source paper "hierarchical coded computation"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "a sequential approximation framework for coded distributed optimization"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-2 (induced by source paper "a droplet approach based on raptor codes for distributed computing with straggling servers"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "block diagonal and lt codes for distributed computing with straggling servers"
    - "a unified coding framework for distributed computing with straggling servers"

Connected through hyperedge HE-3 (induced by source paper "coded computation against processing delays for virtualized cloud based channel decoding"):
  with members:
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "coded iterative computing using substitute decoding"
    - "coded computation over heterogeneous clusters"
    - "on the trade off between computational load and reliability for network function virtualization"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coded computation against straggling decoders for network function virtualization"

Connected through hyperedge HE-4 (induced by source paper "block diagonal and lt codes for distributed computing with straggling servers"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "a unified coding framework for distributed computing with straggling servers"
    - "using straggler replication to reduce latency in large scale parallel computing"
    - "lt code design for inactivation decoding"

Connected through hyperedge HE-5 (induced by source paper "coded computation against straggling decoders for network function virtualization"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "coded network function virtualization fault tolerance via in network coding"
    - "network function virtualization state of the art and research challenges"

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)