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: "sparse tensor algebra as a parallel programming model"
  Abstract: Dense and sparse tensors allow the representation of most bulk data structures in computational science applications. We show that sparse tensor algebra can also be used to express many of the transformations on these datasets, especially those which are parallelizable. Tensor computations are a natural generalization of matrix and graph computations. We extend the usual basic operations of tensor summation and contraction to arbitrary functions, and further operations such as reductions and mapping. The expression of these transformations in a high-level sparse linear algebra domain specific language allows our framework to understand their properties at runtime to select the preferred communication-avoiding algorithm. To demonstrate the efficacy of our approach, we show how key graph algorithms as well as common numerical kernels can be succinctly expressed using our interface and provide performance results of a general library implementation.

Connected through hyperedge HE-1 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "tensor train decomposition on tensorflow t3f"
    - "a practical randomized cp tensor decomposition"
    - "software for sparse tensor decomposition on emerging computing architectures"
    - "spartan scalable parafac2 for large sparse data"
    - "tensor regression meets gaussian processes"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "a unified optimization approach for sparse tensor operations on gpus"

Connected through hyperedge HE-2 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "shared memory parallelization of mttkrp for dense tensors"
    - "communication lower bounds for matricized tensor times khatri rao product"
    - "hptt a high performance tensor transposition c library"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "communication efficient jaccard similarity for high performance distributed genome comparisons"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "a note on the triangle inequality for the jaccard distance"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-4 (induced by source paper "dbcsr a blocked sparse tensor algebra library"):
  with members:
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"

Connected through hyperedge HE-5 (induced by source paper "tensor network complexity of multilinear maps"):
  with members:
    - "spectral methods from tensor networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "matrix multiplication trilinear decompositions apa algorithms and summation"
    - "a framework for practical parallel fast matrix multiplication"
    - "powers of tensors and fast matrix multiplication"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "below all subsets for some permutational counting problems"

Connected through hyperedge HE-6 (induced by source paper "scalar functions and tensor functions a method to import tensor index notation including einstein summation notation"):
  with members:
    - "non linear pattern matching against unfree data types with lexical scoping"

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.MS(Mathematical Software)