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: "survey propagation an algorithm for satisfiability"
  Abstract: We study the satisfiability of randomly generated formulas formed by $M$ clauses of exactly $K$ literals over $N$ Boolean variables. For a given value of $N$ the problem is known to be most difficult with $\alpha=M/N$ close to the experimental threshold $\alpha_c$ separating the region where almost all formulas are SAT from the region where all formulas are UNSAT. Recent results from a statistical physics analysis suggest that the difficulty is related to the existence of a clustering phenomenon of the solutions when $\alpha$ is close to (but smaller than) $\alpha_c$. We introduce a new type of message passing algorithm which allows to find efficiently a satisfiable assignment of the variables in the difficult region. This algorithm is iterative and composed of two main parts. The first is a message-passing procedure which generalizes the usual methods like Sum-Product or Belief Propagation: it passes messages that are surveys over clusters of the ordinary messages. The second part uses the detailed probabilistic information obtained from the surveys in order to fix variables and simplify the problem. Eventually, the simplified problem that remains is solved by a conventional heuristic.

Connected through hyperedge HE-1 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep sets"
    - "streamlining variational inference for constraint satisfaction problems"
    - "inference in probabilistic graphical models by graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "g2sat learning to generate sat formulas"):
  with members:
    - "position aware graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "learning a sat solver from single bit supervision"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "fast counting with tensor networks"):
  with members:
    - "solving and sampling with many solutions satisfiability and other hard problems"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "implementing efficient all solutions sat solvers"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "approximation algorithms for polynomial expansion and low density graphs"
    - "aslib a benchmark library for algorithm selection"
    - "recent advances in graph partitioning"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-4 (induced by source paper "satisfiability threshold for power law random 2 sat in configuration model"):
  with members:
    - "scale free random sat instances"
    - "bounds on the satisfiability threshold for power law distributed random sat"
    - "typical random 3 sat formulae and the satisfiability threshold"

Connected through hyperedge HE-5 (induced by source paper "learning a sat solver from single bit supervision"):
  with members:
    - "recurrent relational networks for complex relational reasoning"
    - "can neural networks understand logical entailment"
    - "recurrent relational networks"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "gated graph sequence neural networks"
    - "neural combinatorial optimization with reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "streamlining variational inference for constraint satisfaction problems"):
  with members:
    - "adversarial constraint learning for structured prediction"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "closing the gap between short and long xors for model counting"
    - "a new look at survey propagation and its generalizations"

Connected through hyperedge HE-7 (induced by source paper "the backtracking survey propagation algorithm for solving random k sat problems"):
  with members:
    - "on the solution space geometry of random constraint satisfaction problems"
    - "a new look at survey propagation and its generalizations"
    - "some remarks on the survey decimation algorithm for k satisfiability"
    - "threshold values of random k sat from the cavity method"
    - "typical random 3 sat formulae and the satisfiability threshold"
    - "on local equilibrium equations for clustering states"

Connected through hyperedge HE-8 (induced by source paper "inside the clustering window for random linear equations"):
  with members:
    - "on independent sets in random graphs"
    - "the set of solutions of random xorsat formulae"
    - "chasing the k colorability threshold"
    - "catching the k naesat threshold"
    - "frozen variables in random boolean constraint satisfaction problems"
    - "the solution space geometry of random linear equations"
    - "reconstruction and clustering in random constraint satisfaction problems"
    - "tight thresholds for cuckoo hashing via xorsat"

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)