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: "sum of squares lower bounds from pairwise independence"
  Abstract: We prove that for every $\epsilon>0$ and predicate $P:\{0,1\}^k\rightarrow \{0,1\}$ that supports a pairwise independent distribution, there exists an instance $\mathcal{I}$ of the $\mathsf{Max}P$ constraint satisfaction problem on $n$ variables such that no assignment can satisfy more than a $\tfrac{|P^{-1}(1)|}{2^k}+\epsilon$ fraction of $\mathcal{I}$'s constraints but the degree $\Omega(n)$ Sum of Squares semidefinite programming hierarchy cannot certify that $\mathcal{I}$ is unsatisfiable. Similar results were previously only known for weaker hierarchies.

Connected through hyperedge HE-1 (induced by source paper "breaking symmetries to rescue sum of squares the case of makespan scheduling"):
  with members:
    - "proximity results and faster algorithms for integer programming using the steinitz lemma"
    - "the power of sum of squares for detecting hidden structures"
    - "sum of squares lower bounds for refuting any csp"
    - "quasi ptas for scheduling with precedences using lp hierarchies"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"
    - "closing the gap for makespan scheduling via sparsification techniques"
    - "on the hardest problem formulations for the 0 1 lasserre hierarchy"

Connected through hyperedge HE-2 (induced by source paper "sum of squares lower bounds for refuting any csp"):
  with members:
    - "lower bounds for csp refutation by sdp hierarchies"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "complexity theoretic limitations on learning halfspaces"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "hardness of robust graph isomorphism lasserre gaps and asymmetry of random graphs"

Connected through hyperedge HE-3 (induced by source paper "sum of squares lower bounds from symmetry and a good story"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on the sum of squares degree of symmetric quadratic functions"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "rounding sum of squares relaxations"

Connected through hyperedge HE-4 (induced by source paper "prediction with a short memory"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "sum of squares lower bounds for refuting any csp"
    - "lower bounds for csp refutation by sdp hierarchies"
    - "training input output recurrent neural networks through spectral methods"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "how to refute a random csp"
    - "character aware neural language models"

Connected through hyperedge HE-5 (induced by source paper "a nearly tight sum of squares lower bound for the planted clique problem"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "sum of squares lower bounds for sparse pca"
    - "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four"
    - "sum of squares lower bounds for planted clique"
    - "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"
    - "hidden cliques and the certification of the restricted isometry property"

Connected through hyperedge HE-6 (induced by source paper "lower bounds for csp refutation by sdp hierarchies"):
  with members:
    - "how to refute a random csp"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "from average case complexity to improper learning complexity"

Connected through hyperedge HE-7 (induced by source paper "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "sum of squares lower bounds for sparse pca"
    - "tight lower bounds for planted clique in the degree 4 sos program"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "sum of squares lower bounds for planted clique"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "sum of squares proofs and the quest toward optimal algorithms"

Connected through hyperedge HE-8 (induced by source paper "how to refute a random csp"):
  with members:
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "analysis of boolean functions"
    - "subsampled power iteration a unified algorithm for block models and planted csp s"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "from average case complexity to improper learning complexity"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "on the complexity of random satisfiability problems with planted solutions"

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)