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: "tensor rank some lower and upper bounds"
  Abstract: The results of Strassen~\cite{strassen-tensor} and Raz~\cite{raz} show that good enough tensor rank lower bounds have implications for algebraic circuit/formula lower bounds. We explore tensor rank lower and upper bounds, focusing on explicit tensors. For odd d, we construct field-independent explicit 0/1 tensors T:[n]^d\to\mathbb{F} with rank at least 2n^{\lfloor d/2\rfloor}+n-\Theta(d\lg n). This improves the lower-order terms in known lower bounds for any odd d\ge 3. We also explore a generalization of permutation matrices, which we denote permutation tensors. We show, by applying known counting lower bounds, that there exist order-3 permutation tensors with super-linear rank as well as order-$d$ permutation tensors with high rank. We also explore a natural class of permutation tensors, which we call group tensors. For any group G, we define the group tensor T_G^d:G^d\to\mathbb{F}, by T_G^d(g_1,\ldots,g_d)=1$ iff $g_1\cdots g_d=1_G. We give two upper bounds for the rank of these tensors. The first uses representation theory and works over ``large'' fields $\mathbb{F}, showing (among other things) that \rank_\mathbb{F}(T_G^d)\le |G|^{d/2}. In the case that d=3, we are able to show that \rank_\mathbb{F}(T_G^3)\le O(|G|^{\omega/2})\le O(|G|^{1.19}), where $\omega$ is the exponent of matrix multiplication. The next upper bound uses interpolation and only works for abelian G, showing that over any field \mathbb{F}$ that $\rank_\mathbb{F}(T_G^d)\le O(|G|^{1+\lg d}\lg^{d-1}|G|). In either case, this shows that many permutation tensors have far from maximal rank, which is very different from the matrix case and thus eliminates many natural candidates for high tensor rank. We also explore monotone tensor rank. We give explicit 0/1 tensors T:[n]^d\to\mathbb{F} that have tensor rank at most $dn$ but have monotone tensor rank exactly n^{d-1}. This is a nearly optimal separation.

Connected through hyperedge HE-1 (induced by source paper "learning representations from imperfect time series data via tensor rank regularization"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "low rank tensor completion for multiway visual data"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "tensor regression networks"
    - "tensor fusion network for multimodal sentiment analysis"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "more barriers for rank methods via a numeric to symbolic transfer"):
  with members:
    - "barriers for rank methods in arithmetic complexity"
    - "query to communication lifting for bpp"
    - "explicit polynomial sequences with maximal spaces of partial derivatives and a question of k mulmuley"
    - "koszul young flattenings and symmetric border rank of the determinant"
    - "on the power of homogeneous depth 4 arithmetic circuits"

Connected through hyperedge HE-3 (induced by source paper "the complexity of matrix multiplication developments since 2014 extended abstract of 2018 oberwolfach complexity meeting plenary lecture"):
  with members:
    - "further limitations of the known approaches for matrix multiplication"
    - "powers of tensors and fast matrix multiplication"
    - "equations for lower bounds on border rank"
    - "new lower bounds for the rank of matrix multiplication"
    - "new lower bounds for the border rank of matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "on multilinear forms bias correlation and tensor rank"):
  with members:
    - "barriers for rank methods in arithmetic complexity"
    - "the chasm at depth four and tensor rank old results new insights"
    - "lower bounds for matrix product"

Connected through hyperedge HE-5 (induced by source paper "barriers for rank methods in arithmetic complexity"):
  with members:
    - "succinct hitting sets and barriers to proving algebraic circuits lower bounds"
    - "on the optimization landscape of tensor decompositions"
    - "explicit polynomial sequences with maximal spaces of partial derivatives and a question of k mulmuley"
    - "towards an algebraic natural proofs barrier via polynomial identity testing"
    - "a note on amortized branching program complexity"
    - "an exponential lower bound for homogeneous depth 5 circuits over finite fields"
    - "unifying known lower bounds via geometric complexity theory"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"

Connected through hyperedge HE-6 (induced by source paper "bayesian robust tensor factorization for incomplete multiway data"):
  with members:
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "infinite tucker decomposition nonparametric bayesian models for multiway data analysis"
    - "robust pca via outlier pursuit"
    - "most tensor problems are np hard"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "decomposing overcomplete 3rd order tensors using sum of squares algorithms"):
  with members:
    - "learning mixtures of gaussians in high dimensions"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "smoothed analysis of tensor decompositions"
    - "online tensor methods for learning latent variable models"

Connected through hyperedge HE-8 (induced by source paper "bayesian cp factorization of incomplete tensors with automatic rank determination"):
  with members:
    - "large margin low rank tensor analysis"
    - "infinite tucker decomposition nonparametric bayesian models for multiway data analysis"

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.CC(Computational Complexity)